WO2016082040A1 - Unified interference rejection combining - Google Patents
Unified interference rejection combining Download PDFInfo
- Publication number
- WO2016082040A1 WO2016082040A1 PCT/CA2015/051234 CA2015051234W WO2016082040A1 WO 2016082040 A1 WO2016082040 A1 WO 2016082040A1 CA 2015051234 W CA2015051234 W CA 2015051234W WO 2016082040 A1 WO2016082040 A1 WO 2016082040A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- impairment
- whitening filter
- interference
- matrix
- signal
- Prior art date
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0426—Power distribution
- H04B7/0434—Power distribution using multiple eigenmodes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/0848—Joint weighting
- H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0868—Hybrid systems, i.e. switching and combining
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/04—Error control
Definitions
- the present document relates to wireless communication, and in one aspect, to signal processing performed in a wireless signal receiver.
- a method and apparatus of interference rejection combining comprises the steps of selecting a detection algorithm based on the signal to interference and noise ratio, generating real- or complex-valued impairment covariance matrix depending on the selected algorithm, calculating an interference reduction metric and comparing the metric with a threshold to determine if IRC shall be enabled, and if IRC is enabled, generating a whitening filter, applying the whitening filter to the received signal and channel matrix, and passing the whitened received signal and channel matrix to the selected standard detection algorithm for signal demodulation.
- a method of recovering data from a received signal implemented at a receiver comprising multiple receiving antennas includes estimating a signal to interference and noise ratio (SINR) of a received signal, selecting, based on the estimated SINR, one of a plurality of detection algorithms available for implementation at the receiver, determining a value of an interference reduction metric, selectively applying a whitening filter to the received signal, wherein the whitening filter is applied only when the value of the interference reduction metric meets a switching criterion, and operating the selected one of the plurality of detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
- At least some of the plurality of detection algorithms have different error rate performances and different computational complexities.
- the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment on multiple receiving antennas.
- a wireless communication receiver to adaptively demodulate a received signal.
- the receiver includes an algorithm control unit that selects one from a plurality of signal detection algorithms available at the receiver, an interference rejection combining (IRC) control unit that determines whether or not to use IRC for the demodulation of a received signal using a detection algorithm that uses spatial correlation among impairment signals that form a part of the received signals, a whitening filter generation unit that computes coefficients of a whitening filter from an estimated impairment correlation matrix and a whitening filter unit that performs, prior to signal detection by the selected one of the plurality of signal detection algorithms, signal filtering using the whitening filter.
- IRC interference rejection combining
- a method of decoding data modulated using an orthogonal frequency division multiplexing (OFDM) technique implemented at a receiver having multiple receive antennas, includes receiving the OFDM signal over multiple antennas, determining a spatial correlation of the impairment component of the received signals, generating coefficients of a whitening filter based on the determined spatial correlation of the impairment, applying the whitening filter to the received signal for de-correlating impairment in the received signal, and performing symbol detection on the de-correlated signal using one of a plurality of signal detection circuits that do not take into account spatial correlation among impairment component signals.
- OFDM orthogonal frequency division multiplexing
- a technique for recovering data from a received signal that includes an interference component is disclosed.
- the technique may be implemented at a receiver comprising multiple receiving antennas.
- the technique includes estimating, at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas, wherein the impairment covariance matrix represents a statistical covariance of the interference plus thermal noise component of the received signal, computing a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas, generating a whitening filter from the Cholesky decomposition, wherein the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment signals across the multiple receiving antennas, applying the whitening filter to the received signal, and operating a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
- FIG. 1 shows an example of a wireless communication system in which inter-cell interference is present.
- FIG. 2 shows an example of a multi-input, multi-output (MIMO) receiver.
- MIMO multi-input, multi-output
- FIG. 3 shows an example of a MIMO receiver that includes Interference Rejection
- FIG. 4 shows an example of a MIMO receiver with a unified IRC unit.
- FIG. 5 shows an example flowchart representation of a process of receiving and decoding digital signals.
- FIG. 6 shows an example flowchart representation of a process of demodulating an orthogonal frequency division multiplexing (OFDM) signal.
- OFDM orthogonal frequency division multiplexing
- FIG. 7 depicts examples of signal processing matrices used during signal reception in a wireless communication receiver.
- FIG. 8 shows an example flowchart of a method of recovering data from a signal received at multiple receive antennas.
- MIMO Multiple Input, Multiple Output
- wireless service providers deploy networks employing 1/1 frequency reuse.
- the frequency reuse could result in an increase in interference from neighbor cells, causing undesirable effects such as lower data throughput, poor speech quality, call dropout, etc.
- effective interference suppressing algorithms in the wireless MIMO receivers have become increasingly important to improve user throughput, especially in cell-edge regions where signals from neighboring cells may overlap with the desired signal.
- Inter-cell interference mitigation is an important issue in cellular networks in general, and is important in 4G and upcoming networks because the way to meet the exponentially growing traffic need is by hyper-dense deployment of cells with low power cells overlaying high power macro cells.
- HetNets heterogeneous networks
- received signal level from a neighbor cell can be much higher than that of the serving cell if the mobile user equipment is located at cell edge.
- An Interference Rejection Combining (IRC) implementation may make use of correlation of interference across multiple receiver antennas to reduce the effect of the interference.
- an IRC receiver based on minimum mean square error criterion referred to as MMSE-IRC advanced receiver
- LTE- A 3rd Generation Partnership Project Long Term Evolution Advanced standard
- an IRC receiver is to meet enhanced requirements for radio signal reception in which the signal is impaired not only by thermal noise but also interference from neighbor cells.
- LTE Release 12 an even more advanced class of receivers called Network Assisted Interference Cancellation an Suppression (NAICS ) is introduced to mitigate even more interference by explicitly cancelling the strongest interference signal(s), or by jointly detecting interference and desired signal.
- NAICS Network Assisted Interference Cancellation an Suppression
- An NAICS receiver can be used to mitigate strong neighbor cell interference signals whereas weaker interference signals can be suppressed more effectively using IRC.
- IRC techniques e.g., as specified in
- the spatial correlation of the impairment (interference plus noise) observed on two or more receive antennas is used to suppress the power of the interference.
- the spatial correlation of impairment component is estimated by calculating individual impairment samples and averaging the impairment samples wherein each impairment sample either includes all received interference signals plus noise, or a subset of all the interference signals plus noise, depending on if the selected detection algorithm detects an interference signal. If the selected detection algorithm detects an interference signal, in addition to detecting the desired user signal, then the interference signal is not included in the impairment sample and vice versa.
- some interference cancellation (IC) techniques e.g., the NAICS receiver technique, explicitly estimate one or more parameters of the interfering signal(s) including their channel matrix and modulation scheme, in addition to estimating the parameters of the desired signal.
- the estimated interfering signal is then subtracted from the received signal to reduce the impact of the interference.
- the resulting difference signal is demodulated to recover the information bits in the desired signal.
- an IRC receiver can work by simply exploiting the spatial correlation of the impairment component of the received signal, and thus can provide a more robust and cost-effective receiver implementation compared to an IC-based implementation.
- the techniques described in the present document can be used for interference suppression in a wireless MIMO-OFDM (Orthogonal Frequency Division Multiplexing) receiver as part of received signal processing.
- the disclosed techniques can be used to unify IRC using different detection algorithms, including NAICS detection algorithms based on either interference cancellation and joint detection.
- the disclosed technique enable a simple migration of current and future receiver designs to incorporate spatial correlation of the impairment among signals received at multiple antennas into the signal detection and symbol decision algorithms.
- the disclosed techniques may be implemented by pre-filtering signals, prior to symbol decisions, using a whitening filter that spatially de-correlates the impairment in the received signals.
- the whitening filter can be calculated by performing decomposition of the impairment correlation matrix resulting in a triangular whitening matrix.
- demodulation is performed for each subcarrier independently, except for transmit diversity mode, where demodulation is done for a pair of adjacent subcarriers.
- the high complexity of MIMO demodulator is due to the very large number of subcarriers that have to be demodulated in every data frame of short duration.
- the demodulator can generate a heavy computation load even using a simple algorithm if the algorithm is executed at a high rate.
- a practical demodulator is typically designed to support an array of different detection algorithms to cover different transmission modes, to reduce power consumption and/or to achieve optimum error rate performance.
- a low-complexity algorithm has better performance than a high-complexity algorithm(s). In other cases, it may be useful to use high-complexity algorithm in order to meet the standard
- an IRC receiver is used in combination with a
- NAICS receiver wherein the strongest interference signal(s) is/ are detected using a NAICS receiver algorithm, whereas weaker interference signals are suppressed using IRC. If a strong interference is to be detected using a NAICS algorithm, this interference signal is not included in the impairment correlation calculation.
- MIMO detection algorithms including NAICS detection algorithms can be categorized based on their design criteria and impairment models.
- algorithms using uncorrelated and correlated impairment model can be referred to as standard algorithms and IRC algorithms, respectively.
- MSE Mean Squared Error
- IRC IRC algorithms
- an implementation that based on the criterion of minimizing Mean Squared Error (MSE) using uncorrelated impairment model may be called a standard MMSE implementation whereas an implementation which minimizes MSE using correlated impairment model may be called MMSE-IRC.
- MSE Mean Squared Error
- MMSE-IRC an implementation which minimizes MSE using correlated impairment model
- a Maximum Likelihood (ML) implementation may maximize the Maximum A Posteriori Probability (MAP) using uncorrelated impairment model
- a ML-IRC algorithm also maximizes MAP, but uses correlated impairment model.
- a NAICS receiver using successive interference cancellation may use MMSE to first detect neighbor cell signal, subtract the detected interference from received signal and then detect the desired signal.
- different IRC techniques may be implemented by separately incorporating the inverse of impairment covariance matrix into a corresponding standard, or non-IRC, algorithms. Such implementations may entail significant changes to the existing, or pre-Release 11, modem chips that typically support only the standard non-IRC detection techniques.
- a MIMO receiver may have to support both IRC and non-IRC mode operation because the IRC gain depends on prevalent interference situation, for example using IRC mode detection in cell center can reduce rather than increase the user data throughput due to the limited accuracy achievable using practical channel and impairment matrix estimation algorithm.
- Orthogonal Frequency Division Multiplexing can be used to convert frequency selective fading radio channel into frequency flat fading channel.
- the smallest unit of time-frequency resource is called a Resource Element (RE ).
- the demodulation of a single subcarrier is relatively simple since r k is the transmitted modulation symbol s k multiplied by the channel frequency response h k plus a noise term v k .
- N D denote the number of receive antennas and transmitted layers, respectively.
- the use of OFDM can greatly simplify MIMO system design and implementation because the x 1 received signal vector is the N D x 1 transmitted symbol vector x k multiplied by the x N D channel frequency response matrix H l k plus a x 1 noise vector n k : x
- the vector l ' k may have been pre-coded at the transmitter using a ⁇ TX x ⁇ D pre-coding matrix, and the resulting signal is transmitted through ⁇ T x transmit antennas.
- H always refers to the composite channel matrix between received signals and ⁇ D transmitted symbols.
- the OFDM symbol and subcarrier indices ⁇ and ⁇ are omitted for brevity, but it should be understood that, in a broadband system, a MIMO receiver needs to demodulate signals ? for potentially a very large number of resource elements to complete the reception of a single data packet. This is the reason why MIMO demodulator is one of the most complex components of a receiver.
- the total frequency resource is divided into resource blocks (RBs) where each RB consists of a certain number of
- the transmitter sends different types of signals using a specific number of RBs.
- a small portion of the allocated resource grid (time-frequency grid) is used to transmit Synchronization Signals (SS), Reference Signals (RSs), and control channels.
- the SS are typically used for time-frequency synchronization;
- RSs are typically used for channel estimation and channel quality measurement; and
- control channels carry information useful to demodulate and decode the user data.
- the larger part of the grid is used to transmit user data using one of the supported transmission modes, including transmit diversity, open- and closed-loop spatial multiplexing and beamforming, etc.
- the impairment vector n which includes interference and thermal noise on
- the receive antennas can be modeled as uncorrelated or correlated random vector.
- the impairment covariance matrix R is the identity matrix scaled by noise variance ⁇ 2
- E ⁇ ⁇ and H denote expectation and transpose-conjugate operator respectively.
- the impairment is modeled as a Hermitian matrix with non-zero off-the-diagonal entries:
- Eq. 3 can be a good model when the impairment only contains thermal noise or if the level of the interference is negligible or if there are a large number of interferers of approximately the same power level. However, if there are several dominant interfering signals, the receiver design should be based on (Eq. 4); otherwise radio performance may degrade significantly.
- n H 2 x 2 + w Eq. 5
- H 2 , x 2 represent the channel matrix and the transmitted symbol vector from the interfering neighbor cell respectively, and ⁇ the thermal noise.
- the corresponding impairment covariance matrix is given by
- the noise covariance matrix estimate R is computed as: where
- FIG. 2 illustrates an exemplary MIMO receiver 200 shown in (FIG. 1) which consists of a radio frontend 202, a baseband processor 204, and a block 206 used for further processing of the received signal.
- the frontend 202 down-converts each of the received signals to baseband, removes Cyclic Prefix (CP) for a block of samples representing an OFDM symbol, and performs Fast Fourier Transform (FFT) to convert the received signal to frequency domain.
- the baseband processor 204 consists of a controller 210, a synchronizer 212, a channel estimator 214, and a MIMO demodulator 216.
- the controller 210 de-maps the received signals to different physical channels, extracts physical signals, and controls the overall baseband processing.
- the synchronizer 212 performs time-frequency synchronization of the receiver 200 which is used to maintain orthogonality of the received signals on different subcarriers.
- Channel estimator 214 picks up received reference signals to estimate the channel matrix at the time-frequency locations corresponding to the reference signals, and then interpolates/extrapolates these channel samples to estimate the channel matrix at resource elements corresponding to data symbols. To use a NAICS algorithm, the channel estimator needs to estimate channel matrix of one or more strongest neighbor cells.
- the channel estimator 214 also estimates the noise variance ⁇ 5 2 and, if interference rejection combining is supported, the impairment covariance matrix R .
- the demodulator 216 uses the received signals, channel matrices, noise variance, and impairment covariance matrix estimate provided by the channel estimator to detect the transmit symbols x and calculate the log-likelihood ratios (LLRs) for each bit in the detected symbols.
- LLRs log-likelihood ratios
- the LLRs are forwarded to the Viterbi and Turbo decoder for error correction decoding.
- the demodulator 216 typically will support an array of different MIMO detection algorithms, including algorithms for supporting NAICS receiver, to cover different transmission modes. To detect a single layer MIMO signal, the Maximum Ratio Combining (MRC) algorithm can be used.
- MRC Maximum Ratio Combining
- SD Sphere Decoding
- the MMSE algorithm has much lower complexity compared to ML and SD algorithm, and additionally often exhibits better error rate performance than ML or SD at low
- SINR Signal-to-Interference-and-Noise-Ratio
- MIMO detection techniques including NAICS detection algorithms
- NAICS detection algorithms can be categorized based on their design criteria and impairment models.
- techniques that use uncorrelated impairment model are referred to as standard algorithms whereas techniques that use correlated impairment model are referred to as IRC algorithms.
- IRC algorithms do not take into account the impairment correlation in the signals received at the different antennas.
- the algorithm which minimizes MSE using uncorrelated impairment model is referred to as standard MMSE or simply MMSE algorithm
- the algorithm which also minimizes MSE but uses correlated model is referred to as MMSE-IRC (see Table 1).
- the ML, ML-IRC, SD, and SD-IRC are all algorithms that are based on the ML criterion.
- SD/SD-IRC typically have lower complexity compared to ML/ML-IRC, because, in SD/SD-IRC algorithm, the search for the ML solution is confined to those vectors x that lie within a hyper sphere with a specific search radius. In other words, the search space is limited.
- Table 1 lists various signal detection algorithms that have been categorized based on the design criterion and the impairment model assumed. Table 1: detection algorithms based on design criterion and impairment model
- FIG. 3 shows an example of a MIMO IRC demodulator 300 in which various receiver-side techniques are shown as independent modules 302.
- various receiver-side techniques are shown as independent modules 302.
- each of these modules is often implemented with a dedicated circuitry.
- the receiver may have made a decision to use a specific signal detection technique, e.g., ones listed in Table 1.
- the IRC control unit 304 may make the decision about whether or not to use IRC.
- such strategy fails to be amenable to changes to detection techniques. For example, including a new detection technique or updating and modifying the algorithmic details of an existing signal detection technique may require significant change to the implementations - e.g., silicon re-design and re-fabrication. Such changes could be expensive and could take a long time to redesign and re-fabricate the products.
- Some disclosed embodiments circumvent the problems by first removing the spatial correlation of the impairment in the received signal by passing the received signal and the channel matrix through a whitening filter. The whitened received signal and channel matrix are then fed to the selected standard algorithm.
- all standard detection algorithms share the same whitening filter. Thus, only a single whitening filter is used to support IRC detection for all the algorithms that have been optimized for the various operating conditions.
- the demodulator can include an IRC switch which can be turned on or off depending on an operational parameter, e.g., the degree of impairment correlation, antenna configuration, etc. When the switch is turned off, the whitening filter unit is bypassed, and received signal and channel matrix are fed directly to the selected standard algorithm.
- An example embodiment of MIMO demodulator 400 comprises an IRC unit 410 that includes an algorithm control unit 402, a IRC control unit 404, a whitening filter generation unit 406, and a whitening filter unit 408.
- Signals, including interference and noise, 428 received at the multiple antennas are fed to the demodulator 400.
- the output of the whitening filter unit 408 can be connected to any of the modules that implements a standard detection algorithm (collectively represented as 412), e.g., MRC module 414, MMSE module 416, ML module 418, SD module 420, SESD module 422, NAICS MMSE-SIC module 424, NAICS ML module 426, etc.
- MRC module 414 e.g., MRC module 414, MMSE module 416, ML module 418, SD module 420, SESD module 422, NAICS MMSE-SIC module 424, NAICS ML module 426, etc.
- MRC module 414 e.g., MRC module 414,
- Eq. 11 shows that the variance of n w is always 1.0 no matter what the original impairment matrix is. This fact can be utilized to simplify the detection process.
- cascading the whitening filter with a standard algorithm is equivalent to the corresponding IRC algorithm. More specifically, cascading the whitening filter with the MMSE algorithm can be equivalent to the MMSE-IRC algorithm, and cascading the whitening filter with the SD algorithm can be equivalent to the SD-IRC algorithm, and so on.
- FIG. 4 shows an exemplary embodiment of MIMO IRC demodulator 400, comprising an algorithm control unit 402, an IRC control unit 404, a whitening filter generation unit 406 and a whitening filter unit 408.
- the algorithm control unit 402 selects the optimum algorithm, MMSE, ML, SD, NAICS MMSE-SIC, etc. The decision about which algorithm to select may be based on SINR and other signal parameters.
- the IRC control unit 404 calculates an interference reduction metric M and compares the metric with a threshold T to determine if IRC should be enabled.
- the interference reduction metric may be related to a value of impairment spatial correlation in the signals received at different antennas.
- the whitening filter generation unit 406 calculates a whitening filter by performing a Cholesky decomposition of the inverse of the impairment covariance matrix.
- the whitening filter unit 408 then applies the whitening filter to the received signal and channel matrix.
- the IRC control unit calculates an interference reduction ratio based on the estimated impairment correlation matrix and compares the metric with a threshold to determine if IRC should be applied.
- the threshold T can be chosen based on the result of simulations, while taking the values of other relevant signal parameters into account such as the estimated SINR, the modulation scheme, and so on.
- the threshold T may be a system-wide constant or may be periodically adjusted based on changes in the wireless network.
- the whitening filter can be generated by first computing the
- Cholesky decomposition of the impairment covariance matrix and then computing the inverse of the triangular Cholesky factor matrix followed by conjugate-transposing the result.
- This method can be used to reduce the computation compared to first computing the inverse of R matrix and then performing Cholesky decomposition of the inverse. This is because inverting a triangular matrix is much simpler than inverting a non-triangular matrix.
- An additional benefit of this method is that, after the Cholesky decomposition of the impairment matrix has been computed, the determinant of the impairment covariance matrix is readily obtained by computing the product of the main diagonal entries of the Cholesky factor matrix and squaring the result. The determinant of the impairment covariance matrix is then used for calculating the interference reduction metric.
- the Cholesky factorization that is calculated using either upper or lower triangular factor matrix can be used to obtain the whitening filter.
- the impairment will be de-correlated whether upper or lower triangular matrix is used. If the power levels of the impairment on receive antennas are very different, however, using upper or lower triangular matrix will have a different effect on the received signals.
- 2 receive antennas with: [0059] In case when upper-triangular whitening filter is used to whiten received signal y ⁇ is: 0 0 y
- Equations 15 and 16 From Equations 15 and 16, it can be seen that in case of upper-triangular filter, the signal y w (l) is the scaled version of the original signal y(V) whereas the signal w (0)is the linear combination of (0) and y(V) . Similarly, in case of lower triangular filter, y w (0) is the scaled version of XO) , whereas y w (l) is the linear combination of (0) and y(V) . In some embodiments, if the impairment power on the second antenna is smaller, i.e. if r ⁇ r 00 , then upper-triangular filter may be used; otherwise lower triangular filter is used. This selectivity of the application of whether to use the upper or the lower triangular filter can be advantageously used to minimize the distortion to the received signal branch that has better quality.
- the performance gain of IRC can be measured in terms of SINR to achieve a certain normalized throughput (for example 70%) with and without using IRC.
- the figure of IRC gain depends on many factors, including location of the mobile terminal, dominant interference power ratio (DIP), which is the ratio of each interferer's power level to the total interference plus thermal noise power, the estimation accuracy of channel, and impairment covariance matrix, etc.
- DIP dominant interference power ratio
- the IRC gain also improves very significantly with increased number of receive antennas, because using more receive antennas enables the receiver to more fully de-correlate the impairment and to reduce the effects of interference to a larger extent.
- the impairment correlation in both antenna and I-Q domain are captured which is especially beneficial when the number of receive antennas is small such as 2.
- the whitening filter is generated from R by computing the Cholesky decomposition of R
- This method of spatial whitening using the real-valued impairment covariance matrix is especially convenient when the SESD algorithm is selected, because the SESD algorithm is also based on converting the complex MIMO to its equivalent real system before detecting. After the whitening operation, the above-described MIMO system has already been converted to a real system, thereby providing partial results for the SESD implementation.
- One advantageous aspect of the techniques disclosed in the present document is that these techniques can significantly reduce computation complexity, when compared with the complexity of an implementation in which the inverse of impairment matrix R "1 is directly incorporated into the respective detection method.
- computations for a whitening operation may be performed. These computations may include multiplication of the received signal vector and channel matrix by a triangular matrix which has real diagonal elements with all elements below the diagonal being zero, i.e., not needing a multiplication. See, e.g., discussion with respect to Equations 15 and 16.
- the additional computation load for IRC based detection involves the multiplication of the received signal vector and channel matrix by the inverse of the impairment correlation matrix which typically has no zero elements.
- the savings can be even more because using the direct
- the full matrix R needs to be multiplied ' ' times by a
- NRX x 1 vector where ⁇ is the number of modulation alphabet size and ⁇ D is the number of transmitted layers and is the number of receive antennas.
- the additional computation complexity is 64 complex vector-matrix multiplications which is impractical to implement using the existing technology.
- the additional computation is exactly the same as with MMSE-IRC, i.e., multiplication of the received signal and channel matrix by the triangular whitening filter matrix.
- the matrix 702 depicts the use of a triangular matrix that can be used to multiple a 4x4 channel matrix H, e.g., in the unified IRC methods described in this document, where the additional computation per carrier is a matrix multiplication where the left matrix is the triangular whitening filter matrix.
- the matrix 704 depicts a full matrix that multiplies a 4x4 channel matrix H in which case additional computation is also a matrix multiplication but both matrices don't have any zero elements.
- FIG. 5 shows an example flowchart of a method 500 for recovering data from a received modulated signal.
- the method 500 may be implemented, e.g., in a receiver device that has multiple receive antennas.
- the method 500 estimating a signal to interference and noise ratio (SINR) of a received signal.
- SINR signal to interference and noise ratio
- the method 500 includes selecting, based on the estimated SINR, one of a plurality of detection algorithms available for implementation at the receiver
- the method 500 includes determining a value of an interference reduction metric.
- the method 500 includes selectively applying a whitening filter to the received signal, wherein the whitening filter is applied only when the value of the interference reduction metric meets a switching criterion.
- the method 500 may estimate the coefficients of the whitening filter by estimating an impairment covariance matrix for received signals and computing a Cholesky decomposition of inverse of the impairment covariance matrix.
- the method 500 includes passing a resulting signal of the selective application of the whitening filter to a module that implements the selected detection algorithm to recover data bits from the received signal.
- a wireless communication receiver to adaptively demodulate a received signal includes an algorithm control unit that selects one from a plurality of signal detection algorithms available at the receiver, an interference rejection combining control unit that determines whether or not to use IRC for demodulation of a received signal using the selected detection algorithm, a whitening filter generation unit that computes coefficients of a whitening filter from an impairment correlation matrix, and a whitening filter unit that performs the whitening prior to signal detection by the selected one of the plurality of signal detection algorithms.
- FIG. 6 shows an example method 600 of receiving modulated data bits.
- the method 600 includes receiving an OFDM signal over multiple antennas (602), determining a spatial correlation of the impairment in the received signals (604), generating coefficients of a whitening filter based on the determined impairment spatial correlation (606), applying the whitening filter to the received signal for de-correlating the impairment in the received signal (608), and performing symbol detection on the de-correlated signal using one of a plurality of standard signal detection circuits (610).
- the signal detection circuits may implement "standard" algorithms as disclosed in the present document.
- the whitening filter is computed by estimating an impairment covariance matrix based on signal received at each of the multiple antennas, wherein the impairment covariance matrix is an N RX X N RX matrix (e.g., Eq. 4), performing a Cholesky decomposition of the impairment covariance matrix, and inverting the Cholesky factor matrix to obtain the whitening filter.
- the impairment covariance matrix is an N RX X N RX matrix (e.g., Eq. 4)
- FIG. 8 depicts an example method 800 of recovering data from a received signal that includes an interference component may be implemented at a receiver comprising multiple receiving antennas.
- the method 800 includes estimating (802), at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas.
- the impairment covariance matrix represents a statistical covariance of the interference plus noise component of the received signal.
- Equation 7 shows an example representation of the impairment covariance matrix that can be calculated during implementation of method 800.
- the method 800 further includes computing (804) a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas.
- the whitened component y w ( ) of the input signal may be dominated by the corresponding received signal, or may be a combination of signals received at different antennas, based on the upper or lower triangular characterization of the Cholesky decomposition.
- the method 800 further includes generating (806) a whitening filter from the
- the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment signals across the multiple receiving antennas.
- the method 800 further includes applying (808) the whitening filter to the received signal.
- the method 800 further includes operating (810) a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
- the interference component is represented as a vector of dimension N RX X 1, wherein N RX corresponds to number of receive antennas of the receiver.
- the method 800 may use the upper triangular Cholesky factor matrix when a measure of bottom entries in the vector is smaller than the measure of top entries in the vector.
- the measure represents magnitude of a corresponding diagonal covariance coefficient entry in the impairment covariance matrix. For example, an example embodiment for 2 receive antennas is discussed in this document with reference to Equations 15 and 16.
- the disclosed and other embodiments, modules and the functional operations described in this document can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or in combinations of one or more of them.
- the disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus.
- the computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more them.
- data processing apparatus encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.
- the apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
- a propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.
- a computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
- a computer program does not necessarily correspond to a file in a file system.
- a program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
- a computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
- the processes and logic flows described in this document can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.
- the processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
- processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
- a processor will receive instructions and data from a read only memory or a random access memory or both.
- the essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
- a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
- mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
- a computer need not have such devices.
- Computer readable media suitable for storing computer program instructions and data include all forms of non- volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
- semiconductor memory devices e.g., EPROM, EEPROM, and flash memory devices
- magnetic disks e.g., internal hard disks or removable disks
- magneto optical disks e.g., CD ROM and DVD-ROM disks.
- the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Quality & Reliability (AREA)
- Radio Transmission System (AREA)
Abstract
A receiver for demodulating wireless signal using multiple receive antennas includes multiple signal detection modules, with each module implementing a non-interference rejection combining signal detection technique. The receiver makes a decision about whether or not to use an interference rejection combining technique, and which signal detection module to use based on the operating conditions of the received signals. When the decision is made to use an interference rejection combining technique, a single whitening filter is used to whiten the received signal prior to sending the whitened signal for processing by the signal detection module. The whitening filter may be calculated as a Cholesky decomposition of an impairment covariance matrix generated from the signals received at the multiple antennas.
Description
UNIFIED INTERFERENCE REJECTION COMBINING
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This patent document claims the benefit of priority of U.S. Provisional Patent
Application No. 62/085,469, filed on November 28, 2014. The entire content of the
before-mentioned patent application is incorporated by reference as part of the disclosure of this application.
TECHNICAL FIELD
[0002] The present document relates to wireless communication, and in one aspect, to signal processing performed in a wireless signal receiver.
BACKGROUND
[0003] Recent years has seen a significant growth in wireless communication. As the number of wireless devices and applications running on the wireless devices goes up, so does the demand for data bandwidth in wireless communication networks. To meet this growing demand for high performance wireless devices and networks, complexity of next generation wireless networks and devices is expected to significantly increase over the currently deployed wireless devices.
SUMMARY
[0004] A method and apparatus of interference rejection combining (IRC) comprises the steps of selecting a detection algorithm based on the signal to interference and noise ratio, generating real- or complex-valued impairment covariance matrix depending on the selected algorithm, calculating an interference reduction metric and comparing the metric with a threshold to determine if IRC shall be enabled, and if IRC is enabled, generating a whitening filter, applying the whitening filter to the received signal and channel matrix, and passing the whitened received signal and channel matrix to the selected standard detection algorithm for signal demodulation.
[0005] In one example aspect, a method of recovering data from a received signal implemented at a receiver comprising multiple receiving antennas is disclosed. The method includes estimating a signal to interference and noise ratio (SINR) of a received signal, selecting,
based on the estimated SINR, one of a plurality of detection algorithms available for implementation at the receiver, determining a value of an interference reduction metric, selectively applying a whitening filter to the received signal, wherein the whitening filter is applied only when the value of the interference reduction metric meets a switching criterion, and operating the selected one of the plurality of detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal. At least some of the plurality of detection algorithms have different error rate performances and different computational complexities. The whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment on multiple receiving antennas.
[0006] In another example aspect, a wireless communication receiver to adaptively demodulate a received signal is disclosed. The receiver includes an algorithm control unit that selects one from a plurality of signal detection algorithms available at the receiver, an interference rejection combining (IRC) control unit that determines whether or not to use IRC for the demodulation of a received signal using a detection algorithm that uses spatial correlation among impairment signals that form a part of the received signals, a whitening filter generation unit that computes coefficients of a whitening filter from an estimated impairment correlation matrix and a whitening filter unit that performs, prior to signal detection by the selected one of the plurality of signal detection algorithms, signal filtering using the whitening filter.
[0007] In yet another example aspect, a method of decoding data modulated using an orthogonal frequency division multiplexing (OFDM) technique, implemented at a receiver having multiple receive antennas, includes receiving the OFDM signal over multiple antennas, determining a spatial correlation of the impairment component of the received signals, generating coefficients of a whitening filter based on the determined spatial correlation of the impairment, applying the whitening filter to the received signal for de-correlating impairment in the received signal, and performing symbol detection on the de-correlated signal using one of a plurality of signal detection circuits that do not take into account spatial correlation among impairment component signals.
[0008] In yet another example aspect, a technique for recovering data from a received signal that includes an interference component is disclosed. The technique may be implemented at a receiver comprising multiple receiving antennas. The technique includes estimating, at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas,
wherein the impairment covariance matrix represents a statistical covariance of the interference plus thermal noise component of the received signal, computing a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas, generating a whitening filter from the Cholesky decomposition, wherein the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment signals across the multiple receiving antennas, applying the whitening filter to the received signal, and operating a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
[0009] These, and other, aspects are disclosed in the present document.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 shows an example of a wireless communication system in which inter-cell interference is present.
[0011] FIG. 2 shows an example of a multi-input, multi-output (MIMO) receiver.
[0012] FIG. 3 shows an example of a MIMO receiver that includes Interference Rejection
Combining (IRC) control units.
[0013] FIG. 4 shows an example of a MIMO receiver with a unified IRC unit.
[0014] FIG. 5 shows an example flowchart representation of a process of receiving and decoding digital signals.
[0015] FIG. 6 shows an example flowchart representation of a process of demodulating an orthogonal frequency division multiplexing (OFDM) signal.
[0016] FIG. 7 depicts examples of signal processing matrices used during signal reception in a wireless communication receiver.
[0017] FIG. 8 shows an example flowchart of a method of recovering data from a signal received at multiple receive antennas.
DETAILED DESCRIPTION
[0018] In recent years, the use of multiple antennas at the transmitter and/or receiver, sometimes collectively called Multiple Input, Multiple Output (MIMO) systems, has become a
pragmatic and cost-effective approach in making high data rate wireless links a reality. To further increase system capacity, wireless service providers deploy networks employing 1/1 frequency reuse. The frequency reuse, however, could result in an increase in interference from neighbor cells, causing undesirable effects such as lower data throughput, poor speech quality, call dropout, etc. As a result, effective interference suppressing algorithms in the wireless MIMO receivers have become increasingly important to improve user throughput, especially in cell-edge regions where signals from neighboring cells may overlap with the desired signal. Inter-cell interference mitigation is an important issue in cellular networks in general, and is important in 4G and upcoming networks because the way to meet the exponentially growing traffic need is by hyper-dense deployment of cells with low power cells overlaying high power macro cells. In such heterogeneous networks (HetNets), received signal level from a neighbor cell can be much higher than that of the serving cell if the mobile user equipment is located at cell edge. An Interference Rejection Combining (IRC) implementation may make use of correlation of interference across multiple receiver antennas to reduce the effect of the interference. For example, an IRC receiver based on minimum mean square error criterion, referred to as MMSE-IRC advanced receiver, was introduced in Release 11 of the 3GPP LTE (3rd Generation Partnership Project Long Term Evolution) Advanced standard (LTE- A). Per LTE- A Release 11, an IRC receiver is to meet enhanced requirements for radio signal reception in which the signal is impaired not only by thermal noise but also interference from neighbor cells. In LTE Release 12, an even more advanced class of receivers called Network Assisted Interference Cancellation an Suppression (NAICS ) is introduced to mitigate even more interference by explicitly cancelling the strongest interference signal(s), or by jointly detecting interference and desired signal. An NAICS receiver can be used to mitigate strong neighbor cell interference signals whereas weaker interference signals can be suppressed more effectively using IRC.
[0019] In comparison with non-IRC based techniques, IRC techniques, e.g., as specified in
Release 11, may put a significant implementation burden on receivers. To add to this, when a receiver is designed to implement multiple IRC techniques, or a mix of IRC and conventional techniques, the circuit area, power consumption, and complexity of implementation may significantly increase.
[0020] In some implementations that use the IRC technique, the spatial correlation of the impairment (interference plus noise) observed on two or more receive antennas is used to suppress
the power of the interference. The spatial correlation of impairment component is estimated by calculating individual impairment samples and averaging the impairment samples wherein each impairment sample either includes all received interference signals plus noise, or a subset of all the interference signals plus noise, depending on if the selected detection algorithm detects an interference signal. If the selected detection algorithm detects an interference signal, in addition to detecting the desired user signal, then the interference signal is not included in the impairment sample and vice versa. By comparison, some interference cancellation (IC) techniques , e.g., the NAICS receiver technique, explicitly estimate one or more parameters of the interfering signal(s) including their channel matrix and modulation scheme, in addition to estimating the parameters of the desired signal. In some IC implementations, the estimated interfering signal is then subtracted from the received signal to reduce the impact of the interference. The resulting difference signal is demodulated to recover the information bits in the desired signal. In contrast to IC, an IRC receiver can work by simply exploiting the spatial correlation of the impairment component of the received signal, and thus can provide a more robust and cost-effective receiver implementation compared to an IC-based implementation.
[0021] The techniques described in the present document can be used for interference suppression in a wireless MIMO-OFDM (Orthogonal Frequency Division Multiplexing) receiver as part of received signal processing. In one aspect, the disclosed techniques can be used to unify IRC using different detection algorithms, including NAICS detection algorithms based on either interference cancellation and joint detection. In one advantageous aspect, the disclosed technique enable a simple migration of current and future receiver designs to incorporate spatial correlation of the impairment among signals received at multiple antennas into the signal detection and symbol decision algorithms.
[0022] The disclosed techniques may be implemented by pre-filtering signals, prior to symbol decisions, using a whitening filter that spatially de-correlates the impairment in the received signals. The whitening filter can be calculated by performing decomposition of the impairment correlation matrix resulting in a triangular whitening matrix.
[0023] In many implementations, demodulation is performed for each subcarrier independently, except for transmit diversity mode, where demodulation is done for a pair of adjacent subcarriers. The high complexity of MIMO demodulator is due to the very large number of subcarriers that have to be demodulated in every data frame of short duration. The
demodulator can generate a heavy computation load even using a simple algorithm if the algorithm is executed at a high rate. To be able to provide satisfactory performance under different operating conditions, a practical demodulator is typically designed to support an array of different detection algorithms to cover different transmission modes, to reduce power consumption and/or to achieve optimum error rate performance. In some cases, for certain operating conditions, a low-complexity algorithm has better performance than a high-complexity algorithm(s). In other cases, it may be useful to use high-complexity algorithm in order to meet the standard
requirements and to achieve high data throughput.
[0024] In some disclosed embodiments, an IRC receiver is used in combination with a
NAICS receiver wherein the strongest interference signal(s) is/ are detected using a NAICS receiver algorithm, whereas weaker interference signals are suppressed using IRC. If a strong interference is to be detected using a NAICS algorithm, this interference signal is not included in the impairment correlation calculation.
[0025] Different MIMO detection algorithms including NAICS detection algorithms can be categorized based on their design criteria and impairment models. For example, algorithms using uncorrelated and correlated impairment model can be referred to as standard algorithms and IRC algorithms, respectively. For example, an implementation that based on the criterion of minimizing Mean Squared Error (MSE) using uncorrelated impairment model may be called a standard MMSE implementation whereas an implementation which minimizes MSE using correlated impairment model may be called MMSE-IRC. A Maximum Likelihood (ML) implementation may maximize the Maximum A Posteriori Probability (MAP) using uncorrelated impairment model, whereas a ML-IRC algorithm also maximizes MAP, but uses correlated impairment model. Similarly, a NAICS receiver using successive interference cancellation may use MMSE to first detect neighbor cell signal, subtract the detected interference from received signal and then detect the desired signal. In some implementations, different IRC techniques may be implemented by separately incorporating the inverse of impairment covariance matrix into a corresponding standard, or non-IRC, algorithms. Such implementations may entail significant changes to the existing, or pre-Release 11, modem chips that typically support only the standard non-IRC detection techniques. A MIMO receiver may have to support both IRC and non-IRC mode operation because the IRC gain depends on prevalent interference situation, for example using IRC mode detection in cell center can reduce rather than increase the user data throughput
due to the limited accuracy achievable using practical channel and impairment matrix estimation algorithm.
[0026] Orthogonal Frequency Division Multiplexing (OFDM) can be used to convert frequency selective fading radio channel into frequency flat fading channel. The transmitter sends K modulation symbols in parallel during an OFDM symbol / ( / = 0,1,... J - 1 , where L is the number of OFDM symbols per timeslot) using NFFT (N >L) sub-carriers. Due to the orthogonality of the subcarriers, the receiver can demodulate received signal r k for each data-carrying subcarrier independently. In OFDM terminology, e.g., as used in the 3GPP documents, the smallest unit of time-frequency resource is called a Resource Element (RE ). The demodulation of a single subcarrier is relatively simple since r k is the transmitted modulation symbol s k multiplied by the channel frequency response h k plus a noise term v k .
[0027] Let and ND denote the number of receive antennas and transmitted layers, respectively. The use of OFDM can greatly simplify MIMO system design and implementation because the x 1 received signal vector is the ND x 1 transmitted symbol vector x k multiplied by the x ND channel frequency response matrix Hl k plus a x 1 noise vector n k :
x
[0028] Depending on the transmission mode being used, the vector l'k may have been pre-coded at the transmitter using a ^TX x ^D pre-coding matrix, and the resulting signal is transmitted through ^Tx transmit antennas. Whether or not pre-coding is performed, H always refers to the composite channel matrix between received signals and ^D transmitted symbols. In the following description, the OFDM symbol and subcarrier indices ^ and ^ are omitted for brevity, but it should be understood that, in a broadband system, a MIMO receiver needs to demodulate signals ? for potentially a very large number of resource elements to complete the
reception of a single data packet. This is the reason why MIMO demodulator is one of the most complex components of a receiver.
[0029] In a typical MIMO-OFDM system, the total frequency resource (spectrum) is divided into resource blocks (RBs) where each RB consists of a certain number of
frequency-consecutive subcarriers during a specific number of time-consecutive OFDM symbols. Within a Transmit Time Interval (TTI), the transmitter sends different types of signals using a specific number of RBs. A small portion of the allocated resource grid (time-frequency grid) is used to transmit Synchronization Signals (SS), Reference Signals (RSs), and control channels. The SS are typically used for time-frequency synchronization; RSs are typically used for channel estimation and channel quality measurement; and control channels carry information useful to demodulate and decode the user data. The larger part of the grid is used to transmit user data using one of the supported transmission modes, including transmit diversity, open- and closed-loop spatial multiplexing and beamforming, etc.
[0030] The impairment vector n , which includes interference and thermal noise on
receive antennas, can be modeled as uncorrelated or correlated random vector. In the first case, the impairment covariance matrix R is the identity matrix scaled by noise variance δ2
[0031] where, E{ }and H denote expectation and transpose-conjugate operator respectively. In the second case, the impairment is modeled as a Hermitian matrix with non-zero off-the-diagonal entries:
[0032] Eq. 3 can be a good model when the impairment only contains thermal noise or if the level of the interference is negligible or if there are a large number of interferers of approximately the same power level. However, if there are several dominant interfering signals,
the receiver design should be based on (Eq. 4); otherwise radio performance may degrade significantly.
[0033] If there is only one interfering neighbor cell, the impairment can be written as n = H2x2 + w Eq. 5 where H2 , x2 represent the channel matrix and the transmitted symbol vector from the interfering neighbor cell respectively, and ω the thermal noise. The corresponding impairment covariance matrix is given by
R = }= E{(H2X2 + w)(H2x2 + wf }= E{H2H2 2 }+ E{WWH } = E{H2H2 2 }+ 52INR∑ Eq. 6
[0034] The following assumptions are used to obtain (Eq. 6) : (1) the interfering signal ¾ and thermal noise are un-correlated, (2) the covariance matrix of ¾ is an identity matrix, and (3) δ2Ι
the covariance matrix of w is Nrx .
[0035] IRC uses the impairment correlations across receive antennas to suppress interference. For example, if H2 in (Eq. 6) is a 2x 1 matrix H2 = [h00 hw J , then the impairment covariance matrix can be expressed as:
[0036] The off-the-diagonal entries of the above covariance matrix are non-zero, because the channel coefficients of the interference signal ¾„ and ¾„ are correlated. To estimate the impairment covariance matrix, the vector el k = [e{ k (0), et k (1), ...el k {N^ - Y)f of impairment samples is computed using the known reference signal symbols: ei,k = y k - Hdi,k » ( k) z Eq. 8 where ΨΚ3 is the set of reference signal symbol indices and d k are known RS symbols and H is the estimated channel matrix. The noise covariance matrix estimate R is computed as:
where |Ψβ5| denotes the number of RS symbols. If there are multiple interference signals with channel matrices H2,H3,...HJ+1 , and corresponding transmit symbol vectors 2, 3,... J+1 and the strongest interference H2x2 are to be detected using a NAICS algorithm, then an impairment sample can be calculated as below where d denotes the RS symbols of the strongest interference signal.
[0037] ei,k = y - Hdi,k - H2dik
[0038] FIG. 2 illustrates an exemplary MIMO receiver 200 shown in (FIG. 1) which consists of a radio frontend 202, a baseband processor 204, and a block 206 used for further processing of the received signal. The frontend 202 down-converts each of the received signals to baseband, removes Cyclic Prefix (CP) for a block of samples representing an OFDM symbol, and performs Fast Fourier Transform (FFT) to convert the received signal to frequency domain. The baseband processor 204 consists of a controller 210, a synchronizer 212, a channel estimator 214, and a MIMO demodulator 216.
[0039] The controller 210 de-maps the received signals to different physical channels, extracts physical signals, and controls the overall baseband processing. The synchronizer 212 performs time-frequency synchronization of the receiver 200 which is used to maintain orthogonality of the received signals on different subcarriers. Channel estimator 214 picks up received reference signals to estimate the channel matrix at the time-frequency locations corresponding to the reference signals, and then interpolates/extrapolates these channel samples to estimate the channel matrix at resource elements corresponding to data symbols. To use a NAICS algorithm, the channel estimator needs to estimate channel matrix of one or more strongest neighbor cells. The channel estimator 214 also estimates the noise variance <52 and, if interference rejection combining is supported, the impairment covariance matrix R . The demodulator 216 uses the received signals, channel matrices, noise variance, and impairment covariance matrix estimate provided by the channel estimator to detect the transmit symbols x and calculate the log-likelihood ratios (LLRs) for each bit in the detected symbols. The LLRs are forwarded to the Viterbi and Turbo decoder for error correction decoding.
[0040] The demodulator 216 typically will support an array of different MIMO detection algorithms, including algorithms for supporting NAICS receiver, to cover different transmission modes. To detect a single layer MIMO signal, the Maximum Ratio Combining (MRC) algorithm can be used. To detect MIMO with 2 or more layers, Minimum Mean Squared Error (MMSE) algorithm, or Maximum Likelihood (ML) algorithm, or Sphere Decoding (SD) algorithm can be used. There are many different kinds of SD algorithms, including two categories of depth-first and breadth-first algorithms. One particular depth-first algorithm is called SESD (Schnorr-Euchner Sphere Decoding) which, in addition to using depth-first tree-search, also converts
complex- valued MIMO into its equivalent real- valued MIMO system before the detection. The MMSE algorithm has much lower complexity compared to ML and SD algorithm, and additionally often exhibits better error rate performance than ML or SD at low
Signal-to-Interference-and-Noise-Ratio (SINR). Therefore, using MMSE at low SINR not only could reduce consumption, but also increase throughput. At high SINR, on the other hand, ML or SD algorithm can outperform MMSE by big margin and the receiver should select either ML or SD algorithm to get high throughput. To detect interference signal in addition to detecting the desired signal, the demodulator also supports NAICS algorithms including NAICS MMSE-SIC and NAICS ML algorithms.
[0041] Different MIMO detection techniques, including NAICS detection algorithms, can be categorized based on their design criteria and impairment models. In the present description, techniques that use uncorrelated impairment model are referred to as standard algorithms whereas techniques that use correlated impairment model are referred to as IRC algorithms. These standard algorithms do not take into account the impairment correlation in the signals received at the different antennas. For example, the algorithm which minimizes MSE using uncorrelated impairment model is referred to as standard MMSE or simply MMSE algorithm, and the algorithm which also minimizes MSE but uses correlated model is referred to as MMSE-IRC (see Table 1).
[0042] The ML, ML-IRC, SD, and SD-IRC are all algorithms that are based on the ML criterion. SD/SD-IRC typically have lower complexity compared to ML/ML-IRC, because, in SD/SD-IRC algorithm, the search for the ML solution is confined to those vectors x that lie within a hyper sphere with a specific search radius. In other words, the search space is limited.
[0043] Table 1 lists various signal detection algorithms that have been categorized based on the design criterion and the impairment model assumed.
Table 1: detection algorithms based on design criterion and impairment model
[0044] It can be seen from Table 1 that, in one aspect, implementing different IRC algorithms amounts to incorporating the inverse of the impairment covariance matrix R"1 into the corresponding standard algorithms. However this method of implementing IRC algorithms by changing the individual algorithms will cause very large increase in chip area, computational complexity, and power consumption. For covering all operational cases, a receiver typically will support both IRC and non-IRC mode operation, because the IRC gain depends on the prevalent interference situation. Using IRC mode detection when the mobile terminal is in the cell center region, for example, can reduce rather than increase the data throughput due to errors in the channel and impairment covariance matrix estimation. This means all formulas in Table 1 would typically be explicitly or implicitly implemented. It can be seen that an IRC algorithm implies significantly more computation compared to the corresponding standard algorithm. For example, demodulating using MMSE-IRC (10-4 in Table 1) uses more computation than using standard MMSE algorithm (10-3 in Table 1).
[0045] FIG. 3 shows an example of a MIMO IRC demodulator 300 in which various receiver-side techniques are shown as independent modules 302. In many practical
implementations, in order to achieve the optimum detection results at various channel conditions, each of these modules is often implemented with a dedicated circuitry. In other words, while the receiver is receiving signals, the receiver may have made a decision to use a specific signal detection technique, e.g., ones listed in Table 1. For example, the IRC control unit 304 may make the decision about whether or not to use IRC. As discussed in this document, such strategy fails to be amenable to changes to detection techniques. For example, including a new detection technique or updating and modifying the algorithmic details of an existing signal detection technique may require significant change to the implementations - e.g., silicon re-design and re-fabrication. Such changes could be expensive and could take a long time to redesign and re-fabricate the products.
[0046] Some disclosed embodiments circumvent the problems by first removing the spatial correlation of the impairment in the received signal by passing the received signal and the channel matrix through a whitening filter. The whitened received signal and channel matrix are then fed to the selected standard algorithm. In some implementations, all standard detection algorithms share the same whitening filter. Thus, only a single whitening filter is used to support
IRC detection for all the algorithms that have been optimized for the various operating conditions. To support adaptive IRC and none-IRC mode detection, the demodulator can include an IRC switch which can be turned on or off depending on an operational parameter, e.g., the degree of impairment correlation, antenna configuration, etc. When the switch is turned off, the whitening filter unit is bypassed, and received signal and channel matrix are fed directly to the selected standard algorithm.
[0047] An example embodiment of MIMO demodulator 400, shown in FIG. 4, comprises an IRC unit 410 that includes an algorithm control unit 402, a IRC control unit 404, a whitening filter generation unit 406, and a whitening filter unit 408. Signals, including interference and noise, 428 received at the multiple antennas are fed to the demodulator 400. The output of the whitening filter unit 408 can be connected to any of the modules that implements a standard detection algorithm (collectively represented as 412), e.g., MRC module 414, MMSE module 416, ML module 418, SD module 420, SESD module 422, NAICS MMSE-SIC module 424, NAICS ML module 426, etc. For example, if MMSE module 416 is selected, the output from the whitening filter unit are fed to the standard MMSE module 416.
[0048] The spatial whitening filter generated by the whitening filter generation unit 406 can be obtained by computing the Cholesky decomposition of the inverse of the impairment covariance matrix R 1 = UHU and multiplying the received signal y and channel matrix Hby the factor matrix U to obtain a whitened received signal yw and the corresponding channel matrix
Hw , resulting in the following equivalent system yw = Uy = U(Hx + n) = UHx+ Un = Hwx + n^ w Eq. 10
[0049] The whitening operation is used to transform the original MIMO system y = Hx + n with correlated impairment n into a new MIMO system yw = Hwx + nw with uncorrelated or white impairment nw . Using the definition of the whitening filter, R 1 = UHU the whiteness of the new impairment nw = Un can be easily proved:
Eq. 11
[0050] Eq. 11 shows that the variance of nw is always 1.0 no matter what the original impairment matrix is. This fact can be utilized to simplify the detection process.
[0051] In some implementations, cascading the whitening filter with a standard algorithm is equivalent to the corresponding IRC algorithm. More specifically, cascading the whitening filter with the MMSE algorithm can be equivalent to the MMSE-IRC algorithm, and cascading the whitening filter with the SD algorithm can be equivalent to the SD-IRC algorithm, and so on. Regarding MMSE-IRC, the equivalence can be proved by feeding the output from the whitening filter yw = Uy, Hw = UH and δ2 = 1.0 to the standard MMSE algorithm which computes estimate x using (10-3 in Table 1), i.e. x = (H" Hw + δ2ΙΝτχ )^ H" yw . Replacing Hw with UH , yw with Uy, δ2 with 1.0 and UHU ith R"1 yields: x = (HHUHUH + IN -lHHUHUy = HH R lH + IN^ yl HH RTly = ^„ Eq. 12
[0052] Regarding ML-IRC, the equivalence can be proved by feeding the output of the whitening filter yw = Uy and Hw = UH to the standard ML algorithm which computes estimate x using (10-5 of Table 1), i.e. x = arg mm(yw - Hwx)H(yw - Hwx) . Replacing Hw with
UH and yw with Uy, and UHU with R"1 yields: x = arg min (yw - H wx (yw - Hwx)
= arg minJ7( ' - H) = arg min (y - Hx) R (y - Hx) = x^^
[0053] The equivalence for other IRC algorithms can be proved in a similar way.
[0054] FIG. 4 shows an exemplary embodiment of MIMO IRC demodulator 400, comprising an algorithm control unit 402, an IRC control unit 404, a whitening filter generation unit 406 and a whitening filter unit 408. Based on the operating conditions, the algorithm control unit 402 selects the optimum algorithm, MMSE, ML, SD, NAICS MMSE-SIC, etc. The decision about which algorithm to select may be based on SINR and other signal parameters. The IRC control unit 404 calculates an interference reduction metric M and compares the metric with a threshold T to determine if IRC should be enabled. In some embodiments, the interference
reduction metric may be related to a value of impairment spatial correlation in the signals received at different antennas.
[0055] In some embodiments, the whitening filter generation unit 406 calculates a whitening filter by performing a Cholesky decomposition of the inverse of the impairment covariance matrix. The whitening filter unit 408 then applies the whitening filter to the received signal and channel matrix.
[0056] In some embodiments, the IRC control unit calculates an interference reduction ratio based on the estimated impairment correlation matrix and compares the metric with a threshold to determine if IRC should be applied. The threshold T can be chosen based on the result of simulations, while taking the values of other relevant signal parameters into account such as the estimated SINR, the modulation scheme, and so on. For example, the threshold T may be a system-wide constant or may be periodically adjusted based on changes in the wireless network.
[0057] In some embodiments, the whitening filter can be generated by first computing the
Cholesky decomposition of the impairment covariance matrix and then computing the inverse of the triangular Cholesky factor matrix followed by conjugate-transposing the result. This method can be used to reduce the computation compared to first computing the inverse of R matrix and then performing Cholesky decomposition of the inverse. This is because inverting a triangular matrix is much simpler than inverting a non-triangular matrix. An additional benefit of this method is that, after the Cholesky decomposition of the impairment matrix has been computed, the determinant of the impairment covariance matrix is readily obtained by computing the product of the main diagonal entries of the Cholesky factor matrix and squaring the result. The determinant of the impairment covariance matrix is then used for calculating the interference reduction metric.
[0058] In various embodiments, the Cholesky factorization that is calculated using either upper or lower triangular factor matrix can be used to obtain the whitening filter. In both cases, the impairment will be de-correlated whether upper or lower triangular matrix is used. If the power levels of the impairment on receive antennas are very different, however, using upper or lower triangular matrix will have a different effect on the received signals. Consider the case of using 2 receive antennas with:
[0059] In case when upper-triangular whitening filter is used to whiten received signal y^ is: 00y
[0061] From Equations 15 and 16, it can be seen that in case of upper-triangular filter, the signal yw(l) is the scaled version of the original signal y(V) whereas the signal w(0)is the linear combination of (0) and y(V) . Similarly, in case of lower triangular filter, yw(0) is the scaled version of XO) , whereas yw(l) is the linear combination of (0) and y(V) . In some embodiments, if the impairment power on the second antenna is smaller, i.e. if r < r00 , then upper-triangular filter may be used; otherwise lower triangular filter is used. This selectivity of the application of whether to use the upper or the lower triangular filter can be advantageously used to minimize the distortion to the received signal branch that has better quality.
[0062] In one aspect, the performance gain of IRC can be measured in terms of SINR to achieve a certain normalized throughput (for example 70%) with and without using IRC. The figure of IRC gain depends on many factors, including location of the mobile terminal, dominant interference power ratio (DIP), which is the ratio of each interferer's power level to the total interference plus thermal noise power, the estimation accuracy of channel, and impairment covariance matrix, etc. The IRC gain also improves very significantly with increased number of receive antennas, because using more receive antennas enables the receiver to more fully de-correlate the impairment and to reduce the effects of interference to a larger extent.
[0063] In some embodiments, if a mobile terminal has only 2 receive antennas, real- valued impairment covariance matrix can be generated by splitting the in-phase (I) and quadrature (Q) component of each impairment sample el k = [re(e{ k )T , im(el k )T f where
re(el k) = [re(el k(0),re(e k(l - - re(eiANR ~ f ,
eu) = [ eu(0),™(eu(l),.. w(eu(N^ - l) , elJc = ylJc - Hdlik and ) c ffi . It can be seen that e^is a 2NRZ x 1 vector. Averaging these vector samples yields the 2NRX x 2NRX real- valued impairment covariance matrix R :
1
R ∑ ei,kei. Eq. 17
Ψ RS I (/ )εΨ,
[0064] Using I-Q splitting, the impairment correlation in both antenna and I-Q domain are captured which is especially beneficial when the number of receive antennas is small such as 2. The whitening filter is generated from R by computing the Cholesky decomposition of R
R = U1 U Eq. 18
[0065] Before the whitening filter U can be applied to the received signal, the original complex- valued MIMO system y = Hx + n is transformed to its equivalent real- valued MIMO system:
y = Hx + n , where re(H) - im(H)
H
[0066] The whitened received signal yw and channel matrix Hw are given by yw = Uy and Hw = Uy Eq. 20
[0067] This method of spatial whitening using the real-valued impairment covariance matrix is especially convenient when the SESD algorithm is selected, because the SESD algorithm is also based on converting the complex MIMO to its equivalent real system before detecting. After the whitening operation, the above-described MIMO system has already been converted to a real system, thereby providing partial results for the SESD implementation.
[0068] One advantageous aspect of the techniques disclosed in the present document is that these techniques can significantly reduce computation complexity, when compared with the complexity of an implementation in which the inverse of impairment matrix R"1 is directly incorporated into the respective detection method.
[0069] In some embodiments using the presently disclosed techniques, computations for a whitening operation may be performed. These computations may include multiplication of the received signal vector and channel matrix by a triangular matrix which has real diagonal elements with all elements below the diagonal being zero, i.e., not needing a multiplication. See, e.g., discussion with respect to Equations 15 and 16. When using the direct method, the additional computation load for IRC based detection involves the multiplication of the received signal vector and channel matrix by the inverse of the impairment correlation matrix which typically has no zero elements.
[0070] It is the zeros in the triangular matrix, which represents the whitening filter, that contribute to the computational saving. The savings are larger with an increasing number of receive antennas and with the size of channel matrix. For example, if there are 8 receive antennas and the channel matrix is 8x8, then there are 28 zeros in the triangular whitening filter matrix which will result in a saving of 28*9 complex multiplications per sub-carrier. Each complex multiplication may be performed using 4 real multiplications and 2 real additions or 6 real operations. The saving per subcarrier is thus 28*9*6 operations. For a LTE system with 1200 data carrying subcarriers, the total saving per packet can be 28*9*6* 1200 = 1,814,400 operations.
[0071] The above savings calculations are applicable to the MMSE-IRC implementation.
For ML-IRC and SD-IRC implementations, the savings can be even more because using the direct
-l \C\N°
implementation method, the full matrix R needs to be multiplied ' ' times by a
NRX x 1 vector where ^ is the number of modulation alphabet size and ^ D is the number of transmitted layers and is the number of receive antennas. Using 64-QAM and ^D ~ ^as an example, the additional computation complexity is 64 complex vector-matrix multiplications which is impractical to implement using the existing technology. By contrast, using the techniques disclosed in the documents, the additional computation is exactly the same as with MMSE-IRC, i.e., multiplication of the received signal and channel matrix by the triangular whitening filter matrix.
[0072] The difference between the computation load using a unified IRC scheme and direct implementation is depicted in the example matrix depictions of FIG. 7. The matrix 702 depicts the use of a triangular matrix that can be used to multiple a 4x4 channel matrix H, e.g., in the unified IRC methods described in this document, where the additional computation per carrier is a matrix multiplication where the left matrix is the triangular whitening filter matrix. The matrix 704 depicts a full matrix that multiplies a 4x4 channel matrix H in which case additional computation is also a matrix multiplication but both matrices don't have any zero elements.
[0073] FIG. 5 shows an example flowchart of a method 500 for recovering data from a received modulated signal. The method 500 may be implemented, e.g., in a receiver device that has multiple receive antennas.
[0074] At 502, the method 500 estimating a signal to interference and noise ratio (SINR) of a received signal.
[0075] At 504, the method 500 includes selecting, based on the estimated SINR, one of a plurality of detection algorithms available for implementation at the receiver
[0076] At 506, the method 500 includes determining a value of an interference reduction metric.
[0077] At 508, the method 500 includes selectively applying a whitening filter to the received signal, wherein the whitening filter is applied only when the value of the interference reduction metric meets a switching criterion. In some embodiments, the method 500 may estimate the coefficients of the whitening filter by estimating an impairment covariance matrix for received signals and computing a Cholesky decomposition of inverse of the impairment covariance matrix.
[0078] At 510, the method 500 includes passing a resulting signal of the selective application of the whitening filter to a module that implements the selected detection algorithm to recover data bits from the received signal.
[0079] In some embodiments, a wireless communication receiver to adaptively demodulate a received signal includes an algorithm control unit that selects one from a plurality of signal detection algorithms available at the receiver, an interference rejection combining control unit that determines whether or not to use IRC for demodulation of a received signal using the selected detection algorithm, a whitening filter generation unit that computes coefficients of a whitening filter from an impairment correlation matrix, and a whitening filter unit that performs
the whitening prior to signal detection by the selected one of the plurality of signal detection algorithms.
[0080] FIG. 6 shows an example method 600 of receiving modulated data bits. The method 600 includes receiving an OFDM signal over multiple antennas (602), determining a spatial correlation of the impairment in the received signals (604), generating coefficients of a whitening filter based on the determined impairment spatial correlation (606), applying the whitening filter to the received signal for de-correlating the impairment in the received signal (608), and performing symbol detection on the de-correlated signal using one of a plurality of standard signal detection circuits (610). For example, the signal detection circuits may implement "standard" algorithms as disclosed in the present document. In some embodiments, the whitening filter is computed by estimating an impairment covariance matrix based on signal received at each of the multiple antennas, wherein the impairment covariance matrix is an NRX X NRX matrix (e.g., Eq. 4), performing a Cholesky decomposition of the impairment covariance matrix, and inverting the Cholesky factor matrix to obtain the whitening filter.
[0081] FIG. 8 depicts an example method 800 of recovering data from a received signal that includes an interference component may be implemented at a receiver comprising multiple receiving antennas. The method 800 includes estimating (802), at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas. The impairment covariance matrix represents a statistical covariance of the interference plus noise component of the received signal. Equation 7 shows an example representation of the impairment covariance matrix that can be calculated during implementation of method 800.
[0082] The method 800 further includes computing (804) a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas. For example, referring to discussion with reference to Equations 15 and 16, the whitened component yw( ) of the input signal may be dominated by the corresponding received signal, or may be a combination of signals received at different antennas, based on the upper or lower triangular characterization of the Cholesky decomposition.
[0083] The method 800 further includes generating (806) a whitening filter from the
Cholesky decomposition. The whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment signals across the
multiple receiving antennas. The method 800 further includes applying (808) the whitening filter to the received signal. The method 800 further includes operating (810) a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
[0084] In some embodiments, the the interference component is represented as a vector of dimension NRX X 1, wherein NRX corresponds to number of receive antennas of the receiver. The method 800 may use the upper triangular Cholesky factor matrix when a measure of bottom entries in the vector is smaller than the measure of top entries in the vector. In some embodiments, the measure represents magnitude of a corresponding diagonal covariance coefficient entry in the impairment covariance matrix. For example, an example embodiment for 2 receive antennas is discussed in this document with reference to Equations 15 and 16.
[0085] It will be appreciated that techniques disclosed in the present document could be used to incorporate multiple IRC-based signal reception techniques without paying significant increase in circuit complexity or power consumption.
[0086] It will further be appreciated that techniques are disclosed to allow a reduced complexity implementation of the whitening filter by avoiding having to perform inversion of an impairment correlation matrix by first computing the Cholesky decomposition of the correlation matrix followed by inverting the Cholesky factor matrix that is triangular.
[0087] It will further be appreciated that the disclosed techniques can be used by implementers to enhance their current hardware implementations of non-IRC based, or standard, signal detection and decoding algorithms with IRC functionality.
[0088] The disclosed and other embodiments, modules and the functional operations described in this document can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or in combinations of one or more of them. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more them. The term "data processing apparatus" encompasses all apparatus, devices, and
machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.
[0089] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
[0090] The processes and logic flows described in this document can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
[0091] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices.
Computer readable media suitable for storing computer program instructions and data include all forms of non- volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
[0092] While this patent document contains many specifics, these should not be construed as limitations on the scope of an invention that is claimed or of what may be claimed, but rather as descriptions of features specific to particular embodiments. Certain features that are described in this document in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or a variation of a sub-combination. Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results.
[0093] Only a few examples and implementations are disclosed. Variations,
modifications, and enhancements to the described examples and implementations and other implementations can be made based on what is disclosed.
Claims
1. A method of recovering data from a received signal implemented at a receiver comprising multiple receiving antennas, the method comprising:
estimating a signal to interference and noise ratio (SINR) of a received signal;
selecting, based on the estimated SINR, one of a plurality of detection algorithms available for implementation at the receiver;
determining a value of an interference reduction metric;
selectively applying a whitening filter to the received signal, wherein the whitening filter is applied only when the value of the interference reduction metric meets a switching criterion; and operating the selected one of the plurality of detection algorithms upon an output of the whitening filter to generate estimates of bits of the data from the received signal;
wherein at least some of the plurality of detection algorithms have different error rate performances and different computational complexities; and
wherein the whitening filter de-correlates received impairment over the multiple receiving antennas by using spatial correlation of the impairment across the multiple receiving antennas.
2. The method of claim 1, further including:
estimating, at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas;
computing a Cholesky decomposition of the impairment covariance matrix; and generating a whitening filter.
3. The method of claim 2, wherein the estimating the impairment covariance includes averaging impairment samples wherein each impairment sample is calculated by subtracting from a received Reference Signal (RS) the contribution from the serving cell, and if one of the Network Assisted Interference Cancellation and Suppression (NAICS) algorithms is selected, the contribution from one or more neighbor cells.
4. The method of claim 2, wherein the computing the Cholesky decomposition of the inverse of impairment covariance matrix comprises using an upper- or a lower- triangular Cholesky factor matrix depending on relative power levels of impairment signals received at the multiple receiving antennas.
5. The method of claim 4, wherein determining the relative power levels of the impairment signals comprises comparing main diagonal entries of the impairment covariance matrix.
6. The method of claim 2, wherein the computing the Cholesky decomposition of the inverse of impairment covariance matrix comprises first computing the inverse of the impairment covariance matrix and then computing the Cholesky decomposition of the inverted matrix.
7. The method of claim 2, wherein the computing the Cholesky decomposition of the inverse of impairment covariance matrix comprises first computing a triangular Cholesky decomposition of the impairment covariance matrix, and then computing an inverse of the triangular Cholesky factor matrix followed by a conjugate-transpose of the result.
8. The method of claim 2, wherein generating the whitening filter includes multiplying a vector comprising the received signal by the Cholesky factor matrix to obtain a whitened signal.
9. The method of claim 8, further including multiplying a matrix representative of the channel by the Cholesky factor matrix to obtain a whitened channel matrix estimate.
10. The method of claim 2, wherein the generating the whitening filter comprises using real- valued impairment covariance matrix to double a dimension of the impairment covariance matrix and to de-correlate impairment simultaneously both in spatial or antenna and in in-phase (I) and quadrature component (Q) domains.
11. The method of claim 10, wherein doubling the dimension of impairment covariance matrix comprises splitting an in-phase (I) and a quadrature component (Q) of the impairment sample
vectors and averaging the sample vectors in time and frequency to compute the said real- valued impairment covariance matrix.
12. The method of claim 10, wherein whitening filter based on real-valued impairment covariance matrix comprises transforming the complex-valued multi-input, multi-output (MIMO) system into the equivalent real-valued MIMO system before applying the whitening filter.
13. The method of claim 2, wherein the generating the whitening filter comprises using complex- valued impairment covariance matrix to generate the whitening filter.
14. The method of claim 1, further comprising passing whitened received signal and whitened channel matrix to the selected standard detection algorithm.
15. The method of claim 1, wherein the determining whether to enable or disable interference rejection combining comprises computing an interference reduction metric and comparing the metric with a threshold .
16. The method of claim 14, wherein the interference reduction metric is based on the impairment correlation matrix by computing the ratio between the determinant of the impairment covariance matrix and the product of the main diagonal entries of the impairment covariance matrix.
17. The method of claim 14, wherein the interference reduction ratio is compared with a threshold to determine whether or not interference rejection combining is to be applied wherein the threshold value can be determined based on the modulation scheme used and a signal to interference and noise ratio (SINR).
18. The method of claim 14, wherein the interference rejection combining is enabled if the interference reduction metric meets a switching criteria, for example, when the interference reduction metric is smaller than the threshold, and otherwise, if the interference reduction metric is equal to or larger than the threshold, interference rejection combining is disabled.
19. A wireless communication receiver to adaptively demodulate a received signal, comprising:
an algorithm control unit that selects one from a plurality of signal detection algorithms available at the receiver;
an interference rejection combining (IRC) control unit that determines whether or not to use interference rejection combining for demodulation in which spatial correlation of impairment component of the received signal is utilized for demodulation;
a whitening filter generation unit that computes coefficients of a whitening filter from an impairment correlation matrix; and
a whitening filter unit that performs, prior to signal detection by the selected one of the plurality of signal detection algorithms, signal filtering using the whitening filter.
20. The wireless communication receiver of claim 19, wherein the algorithm control unit selects one of the plurality of detection algorithms available to the receiver based on an estimate of the signal to interference and noise ratio of the received signals.
21. The wireless communication receiver of 19, wherein the IRC control unit comprises a metric calculator for calculating an interference reduction metric and comparing the metric with a threshold to determine whether interference rejection combing should be used in the detection process.
22. The wireless communication receiver of claim 19, wherein the whitening filter generation unit includes a comparator configured to compare the impairment power levels on different receive antennas to generate a whitening filter using either upper- or lower-triangular Cholesky factor matrix.
23. A method of decoding data modulated using an orthogonal frequency division
multiplexing (OFDM) technique implemented at a receiver having multiple receive antennas, comprising:
receiving the OFDM signal over the multiple antennas;
determining a spatial correlation of an impairment component of the received signals; generating coefficients of a whitening filter based on the determined spatial correlation; applying the whitening filter to the received signal for de-correlating the impairment component of the received signal; and
performing symbol detection on the de-correlated signal using one of a plurality of signal detection circuits that do not use impairment spatial correlation.
24. The method of claim 23, wherein the generating the coefficients of the whitening filter includes:
estimating an NRX X NRX impairment covariance matrix based on signal received at each of the multiple antennas, wherein NRX corresponds to number of receive antennas of the receiver; performing a Cholesky decomposition of the impairment covariance matrix; and inverting the Cholesky factor to obtain the whitening filter.
25. The method of claim 24, wherein the Cholesky factor comprises a triangular matrix.
26. A method of recovering data from a received signal that includes an interference component, implemented at a receiver comprising multiple receiving antennas, the method comprising:
estimating, at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas, wherein the impairment covariance matrix represents a statistical covariance of the interference component of the received signal;
computing a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas;
generating a whitening filter from the Cholesky decomposition, wherein the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment across the multiple receiving antennas;
applying the whitening filter to the received signal; and
operating a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
27. The method of claim 26, wherein the interference component is represented as a vector of dimension NRX X 1 with , wherein NRX correspond to number of receive antennas of the receiver, and wherein the upper triangular Cholesky factor matrix is used when a measure of bottom entries in the vector is smaller than the measure of top entries in the vector.
28. The method of claim 27, wherein the measure represents magnitude of a corresponding diagonal covariance coefficient entry in the impairment covariance matrix.
29. A digital communication receiver apparatus comprising multiple received antennas, a memory, and a processor, wherein the apparatus performs a method of recovering data from signals received at the multiple antennas, wherein the signals include an interference component, the method comprising:
estimating, at the receiver, an impairment covariance matrix for signals received at the multiple receiving antennas, wherein the impairment covariance matrix represents a statistical covariance of the interference component of the received signal;
computing a Cholesky decomposition of the impairment covariance matrix by using one of an upper- and a lower- triangular Cholesky factor matrix depending on relative power levels of impairment component of the signals received at the multiple receiving antennas;
generating a whitening filter from the Cholesky decomposition, wherein the whitening filter de-correlates received interference over the multiple receiving antennas by using spatial correlation of the impairment across the multiple receiving antennas;
applying the whitening filter to the received signal; and
operating a detection algorithm upon an output of the whitening filter to generate estimates of bits of the data from the received signal.
30. The apparatus of claim 29, wherein the interference component is represented as a vector of dimension NRX X 1 with NRX corresponding to number of receive antennas of the receiver, and wherein the upper triangular Cholesky factor matrix is used when a measure of bottom entries in the vector is smaller than the measure of top entries in the vector.
31. The apparatus of claim 30, wherein the measure represents magnitude of a corresponding diagonal covariance coefficient entry in the impairment covariance matrix.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/531,402 US20170324462A1 (en) | 2014-11-28 | 2015-11-25 | Unified interference rejection combining |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201462085469P | 2014-11-28 | 2014-11-28 | |
US62/085,469 | 2014-11-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016082040A1 true WO2016082040A1 (en) | 2016-06-02 |
Family
ID=56073255
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CA2015/051234 WO2016082040A1 (en) | 2014-11-28 | 2015-11-25 | Unified interference rejection combining |
Country Status (2)
Country | Link |
---|---|
US (1) | US20170324462A1 (en) |
WO (1) | WO2016082040A1 (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9686069B2 (en) | 2015-05-22 | 2017-06-20 | ZTE Canada Inc. | Adaptive MIMO signal demodulation using determinant of covariance matrix |
EP3346625A1 (en) * | 2017-01-05 | 2018-07-11 | Ceva D.S.P. Ltd. | System and method for adaptive demodulation of cellular device communications |
IT201800010814A1 (en) | 2018-12-05 | 2020-06-05 | Telecom Italia Spa | Reconfigurable receiver for wireless telecommunications networks |
CN114866381A (en) * | 2021-02-03 | 2022-08-05 | 维沃移动通信有限公司 | Signal processing method, signal processing device, communication equipment and storage medium |
WO2022223100A1 (en) * | 2021-04-20 | 2022-10-27 | Nokia Solutions And Networks Oy | Interference rejection in cooperative multi-point communication |
Families Citing this family (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3297179A1 (en) * | 2016-09-16 | 2018-03-21 | Nxp B.V. | A receiver circuit for suppression of co-channel interference |
CN108471323A (en) * | 2017-02-23 | 2018-08-31 | 深圳市中兴微电子技术有限公司 | A kind of signal detecting method and device based on mimo system |
GB201712840D0 (en) * | 2017-08-10 | 2017-09-27 | Univ Surrey | Apparatus and method for detecting mutually interfering information streams |
US11240685B2 (en) | 2018-01-29 | 2022-02-01 | Samsung Electronics Co., Ltd. | Devices and methods of selecting signal processing algorithm based on parameters |
EP3794733A1 (en) * | 2018-05-16 | 2021-03-24 | Telefonaktiebolaget LM Ericsson (publ) | Interference mitigation |
US11706064B2 (en) * | 2018-05-31 | 2023-07-18 | Indian Institute Of Technology Hyderabad | Method of receiving signal stream and a receiver |
US11025399B2 (en) * | 2019-05-02 | 2021-06-01 | Nokia Technologies Oy | Interference suppression |
CN114097201B (en) | 2019-10-16 | 2023-06-16 | 华为技术有限公司 | Apparatus and method for dynamically reducing interference |
EP4104352A4 (en) * | 2020-02-14 | 2023-11-22 | Qualcomm Incorporated | Reference signal use across physical channels in wireless communications |
KR20220111145A (en) * | 2021-02-01 | 2022-08-09 | 삼성전자주식회사 | Wireless communication device performing inference whitening operation and method of thereof |
US11985014B2 (en) * | 2022-06-03 | 2024-05-14 | Renesas Electronics America Inc. | Digital demodulation for wireless power |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080181342A1 (en) * | 2007-01-29 | 2008-07-31 | Samsung Electronics Co., Ltd. | Apparatus and method for signal detection in multiple input multiple output wireless communication system |
US20130022160A1 (en) * | 2009-02-10 | 2013-01-24 | Samsung Electronics Co., Ltd. | Apparatus and method for adaptive whitening in a multiple antenna system |
-
2015
- 2015-11-25 WO PCT/CA2015/051234 patent/WO2016082040A1/en active Application Filing
- 2015-11-25 US US15/531,402 patent/US20170324462A1/en not_active Abandoned
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080181342A1 (en) * | 2007-01-29 | 2008-07-31 | Samsung Electronics Co., Ltd. | Apparatus and method for signal detection in multiple input multiple output wireless communication system |
US20130022160A1 (en) * | 2009-02-10 | 2013-01-24 | Samsung Electronics Co., Ltd. | Apparatus and method for adaptive whitening in a multiple antenna system |
Non-Patent Citations (1)
Title |
---|
BOARIU ET AL.: "The Cholesky-Iterative Detector: A New Multiuser Detector tor Synchronous CDMA Systems", IEEE COMMUNICATIONS LETTERS, vol. 4, no. 3, March 2000 (2000-03-01), pages 77, Retrieved from the Internet <URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=831030> * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9686069B2 (en) | 2015-05-22 | 2017-06-20 | ZTE Canada Inc. | Adaptive MIMO signal demodulation using determinant of covariance matrix |
EP3346625A1 (en) * | 2017-01-05 | 2018-07-11 | Ceva D.S.P. Ltd. | System and method for adaptive demodulation of cellular device communications |
US10038489B2 (en) | 2017-01-05 | 2018-07-31 | Ceva D.S.P. Ltd. | System and method for adaptive demodulation of cellular device communications |
IT201800010814A1 (en) | 2018-12-05 | 2020-06-05 | Telecom Italia Spa | Reconfigurable receiver for wireless telecommunications networks |
WO2020114885A1 (en) | 2018-12-05 | 2020-06-11 | Telecom Italia S.P.A. | Reconfigurable receiver for wireless communication networks |
US11979258B2 (en) | 2018-12-05 | 2024-05-07 | Telecom Italia S.P.A. | Reconfigurable receiver for wireless communication networks |
CN114866381A (en) * | 2021-02-03 | 2022-08-05 | 维沃移动通信有限公司 | Signal processing method, signal processing device, communication equipment and storage medium |
WO2022223100A1 (en) * | 2021-04-20 | 2022-10-27 | Nokia Solutions And Networks Oy | Interference rejection in cooperative multi-point communication |
Also Published As
Publication number | Publication date |
---|---|
US20170324462A1 (en) | 2017-11-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20170324462A1 (en) | Unified interference rejection combining | |
US9686069B2 (en) | Adaptive MIMO signal demodulation using determinant of covariance matrix | |
CN103685101B (en) | There is the receptor of multi layer interference cancellation | |
US9667372B2 (en) | Mobile communication terminal | |
Lin et al. | Linear precoding assisted blind channel estimation for OFDM systems | |
US9774416B2 (en) | Mobile communication terminal | |
CN109936400B (en) | Adaptive MIMO detection method and system | |
EP2497241B1 (en) | Method and apparatus for interferer parameter estimation using multiple receiver arbitration | |
KR101313862B1 (en) | Apparatus for interference cancellation in mimo receiver, method for interference cancellation in mimo receiver and mimo receiver | |
US8711987B2 (en) | Method and receiver for jointly decoding received communication signals using maximum likelihood detection | |
CN103997473B (en) | A kind of filtering method and relevant apparatus of signal interference | |
Farhang et al. | Pilot decontamination in CMT-based massive MIMO networks | |
US20090296863A1 (en) | Interference Estimator | |
EP1911237A2 (en) | Method, apparatus and computer program product providing widely linear interference cancellation for multi-carrier systems | |
US10097288B2 (en) | Single-stream sliced maximum likelihood aided successive interference cancellation | |
EP3994855A1 (en) | Device and method for dynamically reducing interference | |
EP2628283A1 (en) | A method and apparatus for interference suppression using a reduced-complexity joint detection | |
Abdou et al. | Developing a secondary mobile network in the Palestinian GSM band using cognitive radio | |
Kotzsch et al. | Joint detection and CFO compensation in asynchronous multi-user MIMO OFDM systems | |
Ribeiro et al. | Analytical performance evaluation of Base Station cooperation systems using SC-FDE modulations with iterative receivers | |
Beheshti et al. | Joint compensation of transmitter and receiver IQ imbalance for MIMO-OFDM over doubly selective channels | |
CN105763493A (en) | Signal interference suppression method and apparatus | |
EP2244432A1 (en) | Compensating carrier frequency offsets in OFDM systems | |
US11979258B2 (en) | Reconfigurable receiver for wireless communication networks | |
US8923377B2 (en) | Methods and devices for receipt of imbalanced transmission signal |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 15862599 Country of ref document: EP Kind code of ref document: A1 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 15531402 Country of ref document: US |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 15862599 Country of ref document: EP Kind code of ref document: A1 |