CN115378467A - Power line noise sample extraction method based on diversity signal cancellation - Google Patents
Power line noise sample extraction method based on diversity signal cancellation Download PDFInfo
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Abstract
The invention provides a power line noise sample extraction method based on diversity signal cancellation. The method comprises the following steps: diversity signals to be transmitted are respectively transmitted in a power line and a wireless channel in a diversity mode, and the power line is influenced by impulse noise; respectively carrying out discrete Fourier transform on diversity signals received by receiving ends of a power line and a wireless channel, and then obtaining noise data in the power line signals through cancellation operation; the impulse noise data is removed from the power line signal received at the receiving end by a non-linear function based on an optimal threshold estimate of the noise samples. The invention provides a power line noise sample extraction method based on diversity signal cancellation, which aims at a dual-interface communication architecture. The noise sample is extracted by utilizing the consistency of the power line and the wireless channel transmission signal, the limitation that only the power line is used for independently processing the impulse noise in the traditional hybrid communication is broken through, and the method can be applied to the prediction of the optimal threshold value.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a power line noise sample extraction method based on diversity signal cancellation.
Background
The comprehensive perception and communication of information are the basis and guarantee for constructing the intelligent power grid. The smart grid is wide in coverage range, complex in topological structure and diversified in access equipment, and a single communication technology cannot meet the requirements of various scenes, so that hybrid communication becomes a key technology of the smart grid. The Power Line Communication (PLC) coverage is wide, and no extra wiring is needed; wireless Communication (WLC) is flexible in access and strong in expandability, so that PLC and WLC technologies are closely combined, the system performance of hybrid Communication is improved, and the Wireless Communication network has a wide application prospect in smart power grids and internet of things, and has become one of research hotspots.
However, the power line channel is highly susceptible to Impulse Noise (IN) due to sudden voltage changes caused by the switching on and off of the household appliance and power electronics switches. In particular, the burst noise is generated at a plurality of consecutive sampling points, and affects not only demodulation and decoding of signals but also synchronization performance of power line transmission. How to suppress the impulse noise of the power line channel in the dual-interface communication and further improve the coverage area of the communication system and the reliability of the access thereof is one of the key problems to be solved urgently.
At present, in a dual-interface communication system under the influence of impulse noise in the prior art, researchers mostly focus on technologies such as theoretical performance and signal combination. The Gamma distribution of the wireless fading is approximated to Log Normal (LogN) distribution, so that the mixed fading problem in the power line and wireless parallel communication system is converted into the LogN-LogN same distribution fading problem, and then the theoretical performances such as the system error rate and the like are analyzed. The performances of algorithms such as maximum ratio combination, selective combination and the like in a power line and a wireless parallel communication system are researched by adopting a Middleton Class A pulse noise model. However, these studies often only consider the impact of impulse noise on system performance, and do not address how impulse noise is eliminated. According to the scheme, a multi-stage orthogonal matching tracking recovery algorithm is provided for different types of impulse noise and narrow-band interference, and is used for recovering the impulse noise and the narrow-band interference jointly, but the complexity of parallel processing of double-interface signals is high.
Compared with a compressed sensing algorithm, the impulse noise suppression algorithm based on the nonlinear function obtains attention of researchers due to the advantages of high calculation speed, no need of null sub-carriers and the like. The most widely used non-linear functions include nulling, clipping/deep clipping, and combinations thereof. However, in an Orthogonal Frequency Division Multiplexing (OFDM) system, due to the existence of a Peak-to-Average Power Ratio (PAPR), if an accurate noise threshold cannot be obtained, part of the useful signal will be treated as noise, which greatly limits the performance of the nonlinear function.
In recent years, attention has been paid to the determination of an optimum threshold value for a nonlinear function. In some schemes, burst noise is discretized through an interleaving technology, then a moment estimation is used for obtaining a noise statistical parameter and calculating a noise threshold, but the scheme ignores the influence of channel fading on a system, and the interleaving technology can increase the transmission delay of the system. It is not beneficial to the application of the algorithm in the delay sensitive network. For deterministic signals in impulse noise, the Local Optimal Detection (LOD) algorithm has the best Detection performance at low signal-to-noise ratio, and approaches the theoretical Optimal solution. However, the LOD algorithm needs to know the Probability Density Function (PDF) of the noise accurately, which may not be obtained in the actual scene. Some schemes combine clipping and time/frequency channel equalization, and perform combined clipping before OFDM signal demodulation, and then perform time/frequency equalization, but this method does not significantly improve communication quality, and the threshold is generally obtained through experience.
In the above solutions in the prior art, impulse noise is processed separately in the PLC, and a dual-interface communication environment of a power line and a wireless is not involved.
Disclosure of Invention
The embodiment of the invention provides a power line noise sample extraction method based on diversity signal cancellation, so as to effectively extract noise samples of diversity signals in a power line channel.
In order to achieve the purpose, the invention adopts the following technical scheme.
A power line noise sample extraction method based on diversity signal cancellation comprises the following steps:
diversity signals to be transmitted are diversity-transmitted in a power line and a wireless channel, respectively, the power line being affected by impulse noise;
respectively carrying out discrete Fourier transform on the diversity signals received by the receiving ends of the power line channel and the wireless channel, and then obtaining noise data in the power line diversity signals through cancellation operation;
the impulse noise data is cancelled from the power line diversity signal received at the receiving end by a non-linear function based on an optimal threshold estimate of the noise samples.
Preferably, the diversity signals to be transmitted are diversity-transmitted in a power line and a wireless channel, respectively, the power line being affected by impulse noise, and the diversity signals include:
diversity signal to be transmitted
Respectively carrying out diversity transmission in a power line and a wireless channel, wherein the wireless channel is influenced by Rayleigh fading and white Gaussian noise, the power line channel is influenced by logarithmically normal fading and pulse noise, and the diversity signals are respectively subjected to Orthogonal Frequency Division Multiplexing (OFDM) modulation in the power line and a wireless communication module;
radio channel fading coefficient h W Satisfy Rayleigh distribution, then h W The probability density function PDF of (1) is:
wherein
Is the rayleigh fading variance;
power line channel fading coefficient h P Satisfy LogN distribution, then h P The probability density function PDF of (1) is:
in the formula mu P And
are each lnh P Normalizing the channel fading energy to obtain the mean value and the variance of the channel fading energy
Preferably, the obtaining of the impulse noise data in the power line diversity signal by performing discrete fourier transform on the diversity signals received by the receiving ends of the power line channel and the wireless channel, and then performing cancellation operation includes:
the OFDM modulated time domain symbol of the t-th transmission time slot is
Diversity signal to be transmitted by a transmitting end
Respectively in the channel fading coefficient of
The power line channel and the channel fading coefficient are
In a wireless channel, a power line channel and a receiving end of the wireless channel receive time domain OFDM sampling signals
And
expressed as:
wherein the symbols
Represents a convolution operation;
and
respectively representing time domain Gaussian noise vectors on a power line and a wireless channel, wherein elements in the vectors respectively satisfy the conditions that the mean value is zero and the variance is
And
(ii) a gaussian distribution of;
representing a time-domain impulse noise vector whose elements satisfy a mean of zero and a variance of
Is a Gaussian distribution of
Greater than 1;
will be provided with
And
after the cyclic prefix is removed, discrete Fourier Transform (DFT) is carried out to obtain equivalent frequency domain OFDM signals of the power line channel and the wireless channel
And
respectively expressed as:
wherein F represents the DFT operator and wherein,
denotes an OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N is the number of subcarriers, N] T Which represents the operation of transposition by means of a transposition operation,
and
equivalent channel coefficient matrices respectively representing a power line and a radio channel of a t-th transmission slot,
and
are all diagonal matrices, and
is reversible, make
The left-hand matrix Q can be obtained by:
wherein
To represent
The inverse matrix of (5) and (7) is subtracted to obtain the same signal part in the power line channel receiving end which is cancelled out of the wireless channel
The post frequency domain noise estimate vector Ψ t Comprises the following steps:
wherein
Is a frequency domain vector of the impulse noise of the time domain after DFT transformation,
representing a background noise interference term of the dual interface channel;
let F * Representing the inverse discrete Fourier transform operator, then for Ψ t Performing an inverse discrete Fourier transform F * Ψ t Obtaining a time-domain noise signal vector
Comprises the following steps:
wherein
And
subject to a Gaussian distribution with a mean value of 0, given a known Q, will
Approximate mean of 0 and variance of
A gaussian distribution of (a).
Preferably, the removing the impulse noise data from the power line diversity signal received by the receiving end by the nonlinear function based on the optimal threshold estimation of the noise sample comprises:
determining threshold T using a data sample based adaptive threshold estimation method t Using a zero-setting algorithm in a non-linear function to reduce the amplitude below a threshold T t Is replaced by zero, the reserved amplitude is greater than or equal to T t Of the noise signal
For the extracted noise time domain signal vector
M-th noise element of (1)
The zero-setting transformation is adopted:
where M represents the total number of samples in the tth transmission slot, |, represents the absolute value operation,
a nonlinear transformation result representing impulse noise at the mth sampling point of the tth transmission time slot;
order vector
To represent
Is a result of a non-linear transformation of
For power line signal
Eliminating impulse noise to obtain power line signal without impulse noise
Comprises the following steps:
like equations (5) and (6), F represents a discrete fourier transform operator.
Preferably, the method further comprises:
will be provided with
With signals received by the wireless port
Performing merging treatment to obtain
Estimated symbol obtained by maximum likelihood detection algorithm for kth subcarrier of t transmission time slot
Is composed of
Wherein | · | purple 2 Which represents the 2-norm operation of the signal,
represents an equivalent received signal obtained by combining the power line signal and the wireless signal on the kth subcarrier of the t-th time slot, wherein omega represents a modulation signal constellation point set,
a symbol on a constellation diagram is represented,
which represents the equivalent channel coefficient at the kth sub-carrier of the t-th transmission slot at the receiving end, N is the number of sub-carriers,
meaning that the best x is chosen so that the function f (x) on x is minimal.
Preferably, the threshold value T is determined by adopting an adaptive threshold value estimation method based on data samples t The method comprises the following steps:
minimizing the bit error rate: let Pr (y) t (T t ) Means that the threshold value T is used in one OFDM data frame in the tth transmission slot t The obtained OFDM bit error rate is expressed as:
wherein | · | charging 0 Representing a 0-norm operation (i.e. counting the number of non-zero elements),
indicating the symbol sent by the sending end on the kth subcarrier of the tth transmission slot, where N is the number of subcarriers of one OFDM data frame, the objective function is:
wherein
Indicating a selected noise threshold
So as to relate to T t Error rate Pr (y) t (T t ) Minimum threshold, s.t T. t ≧ 0 indicates satisfaction of the "noise threshold T t The selection range of the receiving terminal is greater than or equal to 0 ″, and the receiving terminal obtains the signal at the t-th transmission time slot
And a channel coefficient matrix
And
based on the construction/update of the real-time noise sample, the error rate problem of the minimum current time is converted into the average error rate problem of the minimum noise sample, and the optimal threshold value of the current time is obtained
The expression is as follows:
wherein L is D Representing the magnitude of the number of noise samples, pr (y) i (T t ) Means that the ith OFDM data frame in the noise sample is related to the noise threshold T t S.t is an abbreviation of subject to, indicating that the constraint condition is satisfied;
when the threshold value is updated in the t-th transmission time slot, the weight lambda of each time slot in the data sample is calculated according to the discount factor gamma i ,i＝1,2,…,L D According to the threshold value T at the T-1 th moment t-1 Combining the weight λ i For data sampleThis D EN Calculating the error rate, and obtaining the threshold value T by gradient descent method t-1 Corresponding error rate gradient value
Using learning rate l R Updating the threshold value of the current time
The solution process of (2) is shown as equation (16):
wherein
Denotes the value of x with respect to f (x) 0 The value of the gradient of (a) is,
is indicated at a noise threshold T t-1 Then, on the kth subcarrier of the ith OFDM data frame in the noise sample, combining the power line signal and the wireless signal to obtain an equivalent received signal,
indicating the symbol transmitted by the transmitting end on the k subcarrier of the ith OFDM data frame among the noise samples,
indicating the number of errors in the ith OFDM data frame in the noise sample,
then the weight of the ith OFDM data frame in the noise sample in the data sample is represented, i.e.
L D N represents the total number of symbols in the data sample, hence
Is the average bit error rate after weighted summation based on data samples.
It can be seen from the technical solutions provided by the embodiments of the present invention that, the method of the present invention provides a power line noise sample extraction method based on diversity signal cancellation for a dual-interface communication architecture. The noise sample is extracted by utilizing the consistency of the power line channel and the wireless channel transmission signal, the limitation that only the power line is used for independently processing the impulse noise in the traditional hybrid communication is broken through, and the method can be applied to the prediction of the optimal threshold value.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a power line noise sample extraction method based on diversity signal cancellation according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a markov chain of a TSMG noise model according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a frame of a power line and wireless dual-interface communication system for performing signal processing at a receiving end according to an embodiment of the present invention;
FIG. 4 shows a probability density according to an embodiment of the present invention
The statistical histogram of (1);
fig. 5 shows a noise data extraction effect based on dual-interface diversity signal cancellation according to an embodiment of the present invention; fig. 5 (a) is a waveform diagram of a power line channel time domain signal, fig. 5 (b) is a waveform diagram of a wireless channel time domain signal, and fig. 5 (c) is a comparison of an extracted signal with an actual IN;
FIG. 6 shows Bit Error Rate (BER) and η @ under the same SNR according to an embodiment of the present invention WCC And η SC The variation with the threshold value T is shown schematically.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and are not construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
In view of the analysis of dual interface communication and non-linear functions. The invention provides a Diversity Signal Cancellation and Adaptive Threshold Estimation (DSC-ATE) pulse noise suppression method based on double interfaces according to independence and difference of a power line and a wireless channel and consistency of Diversity signals. The method comprises the steps of firstly utilizing a wireless channel to assist a power line channel to extract a noise sample, then estimating an impulse noise threshold value in a self-adaptive mode through the noise sample, and finally separating impulse noise from a received signal.
The invention designs an algorithm based on diversity signal cancellation and adaptive threshold estimation to suppress burst noise in a power line channel in dual-interface communication. First, a wireless channel auxiliary power line channel is used for preprocessing in consideration of the consistency of diversity signals, and noise samples are extracted. In order to accurately estimate the noise threshold, the noise threshold is iteratively estimated using the data samples and an objective function minimization/maximization algorithm, thereby determining the location information of the impulse noise. Simulation results show that the method can effectively improve the suppression capability of the burst noise and the reliability of the communication system. The algorithm can be combined with a sparse theory, compressive sensing and the like for research aiming at other noise models such as narrow-band noise, mixed noise and the like, so that the elimination performance of the algorithm on other noises can be researched.
Fig. 1 shows a model of a dual-interface OFDM (Orthogonal Frequency Division Multiplexing) communication system used in the present invention. The transmitted signals are subjected to OFDM modulation in the power line and the wireless communication module respectively, and are transmitted in a power line and a wireless channel in a centralized mode. The wireless channel is affected by Rayleigh fading and Gaussian white noise, and the power line channel is affected by lognormal fading and impulse noise. The power line signal and the wireless signal at the receiving end simultaneously contain the same transmission information, and only the power line signal has impulse noise with larger amplitude, so that the impulse noise data in the power line signal can be obtained by carrying out cancellation operation on the signal after Discrete Fourier Transform (DFT). That is, impulse noise data can be obtained with the aid of diversity signals transmitted over a wireless channel. And then, the power line and the wireless signal are subjected to combination processing and decoding output. And storing the extracted pulse noise data, namely establishing a data sample space to provide data support for the algorithm or other algorithms.
Radio channel fading coefficient h W Satisfy Rayleigh distribution, then h W The probability density function PDF of (1) is:
wherein
Is the variance of rayleigh fading.
Power line channel fading coefficient h P Satisfy LogN distribution, then h P Has a probability density function PDF of
In the formula of P And
are each lnh P Mean and variance of. Normalizing the channel fading energy to obtain
Based on the correlation of burst noise in time, the invention establishesA Two-State Markov-Gaussian (TSMG) model. Fig. 2 is a schematic diagram illustrating a markov chain of a TSMG noise model according to an embodiment of the present invention. As shown in FIG. 2, in the TSMG, the statistical behavior of the power line noise is made to be the noise state s k E { B, I }, where B represents that the channel is only interfered by background noise and I represents that the channel is affected by impulse noise. The noise state generation process may use one s 1 ,s 2 ,…,s K A sequence characterization of. For this model, the K +1 th noise state s K+1 Can be represented by a first order Markov process:
wherein s is k And s k+1 Respectively representing the noise states, P(s), of the samples at different times K+1 ) Representing the K +1 th sample point, the noise being in state s K+1 The probability value of (2). s 1 Representing the initial noise state s without loss of generality 1 Randomly between a background noise state and an impulse noise state.
Thus, the state change process of the noise can be represented by the state transition probability Ps k s k+1 ＝P(s k+1 |s k ) And (4) showing. According to the state transition probability, respectively obtaining the probability P that the noise is in the state I and the state B I And P B Is composed of
Wherein P is BI Representing the transition probability, P, of state B to state I IB Is the transition probability of state I to state B. s k = I denotes at kth state (sample point), noise state s k An impulse noise state.
s k = B indicates the noise state s at the k-th state (sampling point) k Is a background noise state. Available parameters
To describe the size of the noisy memories,
indicating that the noise is memoryless, an
Indicating that the noise has memory, i.e. there is a temporal correlation.
In the following description, the power line channel is subjected to background noise
Is still subject to impulse noise
Interference of (2) requires a Markov sequence s in the TSMG model 1 ,s 2 ,…,s K Characterization, and background noise
And impulse noise
Is constrained by the gaussian distribution of the different parameters.
Fig. 3 is a schematic diagram of a frame of a power line and wireless dual-interface communication system for performing signal processing at a receiving end according to an embodiment of the present invention. Firstly, removing a power line and a Cyclic Prefix (CP) of a wireless time domain signal at a receiving end, and performing ideal channel estimation by using a pilot signal; because the two channels transmit the same information, under ideal channel estimation, the data part of the power line signal, which is the same as that of the wireless signal, can be removed through cancellation operation, so that a noise sample in the power line signal is reserved; then extracting the impulse noise in the noise sample through a nonlinear function, and eliminating the impulse noise from the power line signal; and finally, combining, decoding and outputting the diversity signals in the power line and the wireless channel.
The OFDM modulated time domain symbol of the t-th transmission time slot is
By a channel fading coefficient of
And
after the same diversity signals are transmitted by the power line and the wireless parallel channel, the time domain OFDM sampling signals received by the receiving end
And
can be expressed as:
wherein the symbols
Which represents a convolution operation, is a function of,
and
respectively representing time domain Gaussian noise vectors on a power line and a wireless channel, wherein elements in the vectors respectively satisfy that the mean value is zero and the variance is
And
(ii) a gaussian distribution of;
representing a time-domain impulse noise vector whose elements satisfy a mean of zero and a variance of
Is a Gaussian distribution of
Is greater than 1. Removing CP from the OFDM signal, and performing DFT to obtain equivalent frequency domain OFDM signal
And
respectively expressed as:
wherein F represents the DFT operator and wherein,
denotes an OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N is the number of subcarriers, N] T Representing a transpose operation.
And
the power line of the t-th transmission time slot and the wireless equivalent channel coefficient matrix are respectively represented, and because CP is utilized to resist frequency selective fading caused by multipath channels, the invention ignores the influence of intersymbol interference, so that
And
are all diagonal matrices, and
it is reversible. Order to
Left-hand multiplication matrix Q can be obtained
Wherein
Represent
The inverse matrix of (c). Subtracting the formula (8) and the formula (10) to obtain the same signal part in the power line channel receiving end which is cancelled out of the wireless channel
The resulting frequency domain noise estimate vector Ψ t Comprises the following steps:
wherein
Is a frequency domain vector of the time domain impulse noise after DFT transformation,
representing the background noise interference term of the dual interface channel.
To psi t Performing an inverse discrete Fourier transform F * Ψ t Obtaining a noisy time-domain signal vector
Comprises the following steps:
wherein the content of the first and second substances,
and
a gaussian distribution with a mean value of 0 is obeyed. Under the condition of known Q, the method can be used for
Approximated as a mean of 0 and a variance of
A gaussian distribution of (a). The present invention performs statistical analysis on noisy data samples, and fig. 4 is a probability density provided by an embodiment of the present invention
Is a statistical histogram of
Distribution of (2) and Gaussian distribution
The heights are overlapped.
Fig. 5 shows a Noise data extraction effect based on dual-interface diversity Signal cancellation according to an embodiment of the present invention, wherein a Signal-to-Noise Ratio (SNR) S NR =10dB, and the other system parameters are shown in table 1. Fig. 5 (a) and 5 (b) show time domain signals received by a power line and a wireless channel, respectively, and fig. 5 (c) shows a processed signal
And comparing the result with the actual impulse noise. As can be seen from fig. 5 (a) and 5 (b), the PAPR exists in the OFDM system, and it is difficult to directly acquire the position information of the impulse noise in the power line channel. After the diversity signal cancellation, as shown in FIG. 5 (c), the diversity signal is cancelled
Only background interference is left, the influence of the PAPR is minimal, and therefore, the position information of the impulse noise can be acquired using a nonlinear transformation. It is clear that the noise data in fig. 5 (c) can be saved to provide a big data support for the optimal threshold estimation algorithm.
The signal processing process based on the nonlinear function provided by the embodiment of the invention comprises the following steps: as can be seen from FIG. 5 (c), the amplitude is reduced to be less than the threshold T by using the zero-setting algorithm in the nonlinear function t Is replaced by zero, the reserved amplitude is greater than or equal to T t Is the simplest and most effective. Thus to the extracted noisy time-domain signal vector
M-th noise element of (1)
The zero-setting transformation is adopted:
where M represents the total number of samples in the tth transmission slot, | · | represents an absolute value operation,
representing the result of the nonlinear transformation of the impulse noise at the mth sampling point of the tth transmission slot.
Order vector
To represent
Is a result of a non-linear transformation of
For power line signal
Pulse noise elimination is carried out, and a power line signal without pulse noise can be obtained
Comprises the following steps:
like equations (8) and (9), F represents a discrete fourier transform operator.
The power line signal after pulse noise removal is then used
And wireless signals
Merging to obtain y t Finally, the estimated symbol obtained by the maximum likelihood detection algorithm is adopted for the kth subcarrier of the tth transmission time slot
Comprises the following steps:
wherein | · | purple 2 Which represents the 2-norm operation,
represents an equivalent received signal obtained by combining the power line signal and the wireless signal on the kth subcarrier of the t-th time slot, wherein omega represents a modulation signal constellation point set,
a symbol on a constellation diagram is represented,
which represents the equivalent channel coefficient at the kth sub-carrier of the t-th transmission slot at the receiving end, N is the number of sub-carriers,
meaning that the best x is chosen so that the function f (x) on x is minimal.
The nonlinear function adaptive threshold estimation process provided by the embodiment of the invention comprises the following steps:
adaptive threshold design based on traditional methods
How to accurately estimate the threshold T in equation (13) is crucial to the algorithm performance, wherein the conventional Weighted Combination Criterion (WCC) and Siegert Criterion (SC) balance the detection probability and false alarm probability of impulse noise to obtain the optimal threshold. Extensive research has been carried out due to the small number of parameters required for WCC and SC.
Definition P a For good detection probability of impulse noise, P b Is the false alarm probability, where the composite objective function is η WCC ＝P a -P b Then the optimum threshold T of the WCC criterion WCC Comprises the following steps:
the key step of the WCC criterion is to determine the optimal threshold T by targeting the composite function of the detection probability and the false alarm probability as shown in equation (16) WCC Eta is given by the formula (16) WCC The larger the false alarm probability and the smaller the missed detection probability. At a known power of impulse noise
Power of and background interference
Under the conditions of (1), P may be given separately a And P b Is then calculated from the target function eta WCC The optimum threshold T can be obtained by calculating the partial derivative of the threshold T WCC Comprises the following steps:
similarly, let the objective function η SC ＝P I P a +P B (1-P b ) The optimal threshold T of the SC criterion can be obtained SC 。
FIG. 6 shows Bit Error Rate (BER) and η according to the embodiment of the present invention WCC And η SC The variation with the threshold value T is shown schematically. It can be seen that there is an optimum threshold T with the goal of BER minimization * But the optimum threshold value T * And threshold T obtained by two criteria WCC 、T SC There is a certain deviation. This is because both criteria solve the threshold with the detection probability and the false alarm probability as the synthetic target, rather than directly using BER as the optimization target.
Adaptive threshold estimation based on data samples
Conventional threshold estimation algorithms require accurate noise parameters. Due to the non-stationarity of the communication environment, the acquisition of parameters is difficult, and a certain deviation exists between the threshold value acquired by the traditional algorithm and the real optimal solution.
Inspired by data-driven machine learning and transfer learning, obtaining the optimal threshold of a nonlinear function through a noise sample and gradient descent method is a new idea for processing impulse noise. Compared with the traditional algorithm, the optimization algorithm based on the noise sample can continuously adjust parameters according to the actual environment so as to achieve the purposes of directly optimizing the target function and reducing the deviation. Since there is no disclosed impulse noise database, the diversity cancellation algorithm proposed by the present invention can be used to construct a noise sample database and used for optimal threshold estimation. In order to ensure the real-time performance of the algorithm, the threshold value needs to be updated iteratively by using the latest noise sample so as to cope with the change of the environment or the parameter.
The optimal threshold estimation algorithm is closely related to the selection of the objective function, and a specific objective function needs to be established for different communication scenes and performance indexes. The invention mainly provides the following two forms:
1) Minimization of BER: in the OFDM communication system, the performance is most commonly evaluated using the BER, which is one of the indicators in the Quality of Service (QoS). The invention compares the symbol obtained by the decoding judgment of the formula (15) with the symbol sent by the sending end, thereby obtaining the error rate and enabling Pr (y) t (T t ) Means that the threshold value T is used at the T-th time t The obtained error rate of one OFDM frame can be expressed as:
wherein
Is indicated at a noise threshold T t On the kth subcarrier of the next tth transmission time slot, combining the power line signal and the wireless signal to obtain an equivalent receiving signal, | · | | | sweet wind 2 Representing 2-norm operation, | ·| luminance 0 Representing a 0-norm operation (i.e., counting the number of non-zero elements), omega represents the set of modulation signal constellation points,
a symbol on a constellation diagram is represented,
indicating the equivalent channel coefficient on the kth sub-carrier of the tth transmission slot at the receiving end,
indicating the symbol transmitted by the transmitting end on the kth subcarrier of the tth transmission slot. The same reasoning is that N is the number of subcarriers of one OFDM data frame,
meaning that the best x is chosen such that the function value f (x) for x is minimal. The objective function is then:
wherein Pr (y) t (T t ) Means that one OFDM frame uses the threshold T at the tth transmission slot t The obtained OFDM code error rate is calculated,
then indicates the selected noise threshold
So as to be related to T t Error rate Pr (y) t (T t ) ) minimum threshold.
2) Maximizing the transmission rate R t : the transmission rate and the channel quality are directly related. After the channel bandwidth is normalized, the equivalent signal-to-noise ratio can be improved by eliminating the impulse noise in the channel, and the transmission capability of the channel is indirectly improved. The noise threshold can thus be optimized based on short packet theory with the goal of maximizing the transmission rate of the finite length code, which is at the noise threshold T t Lower average transmission rate R t (T t ) Can be expressed as
Wherein
Indicating that the receiving end is at the noise threshold T t Equivalent signal-to-noise ratio of k sub-carrier of the next t transmission time slot, V represents channel dispersion, L represents coding length, and Q -1 (. Cndot.) is a complementary error function, and ε represents the bit error rate, then the objective function is
Wherein
Means that the optimum x is selected so that the function value f (x) with respect to x is maximum.
The characteristics of different objective functions are similar, a similar optimization design method can be adopted, and the optimization process with the minimum BER as the objective function is introduced.
In the t-th transmission time slot, the receiving end can only obtain signals
And a channel coefficient matrix
And
therefore, the bit error rate of the t-th transmission time slot cannot be directly obtained, and the threshold value cannot be optimized. Based on the construction/updating of the real-time noise sample, the invention converts the error rate problem of the minimized current time into the error rate problem of the minimized noise sample, thereby obtaining the optimal threshold value of the current time slot
The expression is
Wherein L is D Representing the magnitude of the number of noise samples. Under random channel conditions, L D The selection of (a) needs to take into account trade-offs in computational accuracy and complexity.
When the threshold value is updated in the t-th transmission time slot, the weight lambda of each time slot in the data sample is calculated according to the discount factor gamma i ,i＝1,2,…,L D . And then according to the threshold value T of the previous time slot (i.e. T-1 time slot) t-1 Combining the weights λ i For data sample D EN Calculating the error rate, and then obtaining the threshold value T by a gradient descent method t-1 Corresponding error rate gradient value
Last using learning rate l R Updating the threshold value at the current moment
1 ). In the appendix are introduced T t The result of the solving process of (2) is shown in equation (23)
Wherein
Denotes the value of x with respect to f (x) 0 The value of the gradient of (a) is,
is indicated at a noise threshold T t-1 And combining the power line signal and the wireless signal on the kth subcarrier of the ith OFDM data frame in the noise sample to obtain an equivalent received signal.
RepresentIn the noise samples, the symbol sent by the transmitting end on the k subcarrier of the ith OFDM data frame.
Indicating the number of errors in the ith OFDM data frame in the noise sample.
Then the weight of the ith OFDM data frame in the noise sample in the data sample is represented, i.e.
L D N represents the total number of symbols in the data sample, hence
Is the average bit error rate after weighted summation based on data samples.
In addition, the present invention employs a queue storage (first-in first-out criterion) to save/update noise samples, thereby ensuring the validity of noise data. The specific steps of the algorithm are as follows:
through the above steps, a more accurate threshold value can be obtained. Reconstruction of impulse noise by equation (13)
And subtracting the reconstructed impulse noise from the frequency domain OFDM signal before demodulation by the formula (14), and then carrying out operations such as merging processing, decoding judgment and the like on the received signal from which the impulse noise is removed to obtain a data symbol.
The characteristics of the algorithm of the present invention are analyzed in terms of computational complexity, threshold accuracy, update rate, and the like.
1) And (3) complexity analysis: in the process of taking the minimum BER as a target, the complexity of the algorithm mainly relates to three operations of judgment, addition and subtraction and multiplicationAnd (4) calculating. The judgment and addition-subtraction are linear operations with the highest operation efficiency, mainly include formula (13) nonlinear transformation, (14) impulse noise removal and (18) decoding judgment, and the multiplication mainly includes
DFT, etc. Through analysis, in one iteration calculation, the complexity of linear operation quantity and multiplication operation is O (kN).
2) Threshold accuracy: the noise sample is directly from the physical environment where the communication is located, the communication quality at the current moment can be accurately reflected, and a more accurate threshold value is obtained on the premise that a noise model and parameters are not needed. The traditional algorithm needs to obtain statistical data and relevant models of the environment, and the phenomenon that the actual environment is not matched with the models can occur.
3) The updating rate is as follows: when the environment changes, it is required to quickly adapt to the dynamic change of the channel or the environment. By means of the learning rate and the discount factor, the DSC-ATE algorithm can dynamically adjust the threshold value according to environmental changes, and the deviation from the actual threshold value is reduced. The traditional algorithm is constrained by a statistical model, and when the model parameters are not changed, the threshold value cannot be adjusted quickly.
In summary, compared with the existing method, the embodiments of the present invention mainly contribute to the following:
1) Aiming at a dual-interface communication architecture, a power line noise sample extraction method based on diversity signal cancellation is innovatively provided. The noise sample is extracted by utilizing the consistency of the power line and the wireless transmission diversity signal, the limitation that only the power line is used for independently processing the impulse noise in the traditional hybrid communication is broken through, and the method can be applied to the prediction of the optimal threshold value.
2) Based on diversity transmission, a sample space of impulse noise is constructed, and the sample space comprises noise samples with low signal-to-noise ratio (abnormal communication); and by combining a nonlinear function, an optimal threshold estimation algorithm based on a noise sample is provided, so that the bit error rate minimization of a communication system can be realized, and the prior information of impulse noise is not required.
3) For the problem of real-time change of the optimal threshold in a non-stationary environment, the convergence speed of the algorithm is adjusted by introducing parameters such as a learning rate and a discount factor, so that the algorithm can better utilize noise samples, the robustness is improved, and the effective compromise of the algorithm between the robustness and the convergence speed is realized.
Those of ordinary skill in the art will understand that: the figures are schematic representations of one embodiment, and the blocks or processes shown in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, apparatus or system embodiments, which are substantially similar to method embodiments, are described in relative ease, and reference may be made to some descriptions of method embodiments for related points. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (6)
1. A power line noise sample extraction method based on diversity signal cancellation is characterized by comprising the following steps:
diversity signals to be transmitted are diversity-transmitted in a power line and a wireless channel, respectively, the power line being affected by impulse noise;
respectively carrying out discrete Fourier transform on the diversity signals received by the receiving ends of the power line channel and the wireless channel, and then obtaining noise data in the power line diversity signals through cancellation operation;
the impulse noise data is cancelled from the power line diversity signal received at the receiving end by a non-linear function based on an optimal threshold estimate of the noise samples.
2. The method of claim 1, wherein the diversity signals to be transmitted are diversity transmitted in a power line and a wireless channel, respectively, the power line being affected by impulse noise, comprising:
diversity signal to be transmitted
Respectively carrying out diversity transmission in a power line and a wireless channel, wherein the wireless channel is influenced by Rayleigh fading and white Gaussian noise, the power line channel is influenced by logarithmically normal fading and pulse noise, and the diversity signals are respectively subjected to Orthogonal Frequency Division Multiplexing (OFDM) modulation in the power line and a wireless communication module;
radio channel fading coefficient h W Satisfy Rayleigh distribution, then h W The probability density function PDF of (1) is:
wherein
Is the rayleigh fading variance;
power line channel fading coefficient h P Satisfy LogN distribution, then h P The probability density function PDF of (1) is:
in the formula of P And
are each lnh P Normalizing the channel fading energy to obtain
3. The method of claim 1, wherein the obtaining impulse noise data in the power line diversity signal by performing discrete fourier transform on the diversity signals received by the receiving ends of the power line channel and the wireless channel and performing cancellation operation comprises:
the OFDM modulated time domain symbol of the t-th transmission time slot is
Diversity signal to be transmitted by a transmitting end
Respectively in the channel fading coefficient of
The power line channel and the channel fading coefficient are
In a wireless channel, a power line channel and a receiving end of the wireless channel receive time domain OFDM sampling signals
And
expressed as:
wherein the symbols
Represents a convolution operation;
and
respectively representing time domain Gaussian noise vectors on a power line and a wireless channel, wherein elements in the vectors respectively satisfy the conditions that the mean value is zero and the variance is
And
(ii) a gaussian distribution of;
representing a time-domain impulse noise vector whose elements satisfy a mean of zero and a variance of
Is gaussian distribution of (a) and
greater than 1;
will be provided with
And
after the cyclic prefix is removed, discrete Fourier Transform (DFT) is carried out to obtain equivalent frequency domain OFDM signals of the power line channel and the wireless channel
And
respectively expressed as:
wherein F represents the DFT operator and wherein,
denotes an OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N is the number of subcarriers, N] T Which represents the operation of transposition of the image,
and
equivalent channel coefficient matrices respectively representing the power line and the wireless channel of the tth transmission slot,
and
are all diagonal matrices, and
is reversible, make
The left-hand matrix Q can be obtained by:
wherein
To represent
The inverse matrix of (5) and (7) is subtracted to obtain the same signal part in the power line channel receiving end which is cancelled out of the wireless channel
The post frequency domain noise estimate vector Ψ t Comprises the following steps:
wherein
Is a frequency domain vector of the impulse noise of the time domain after DFT transformation,
representing a background noise interference term of the dual interface channel;
let F * Representing the inverse discrete Fourier transform operator, then for Ψ t Performing an inverse discrete Fourier transform F * Ψ t Obtaining a time-domain noise signal vector
Comprises the following steps:
wherein
And
subject to a Gaussian distribution with a mean value of 0, given a known Q, will
Approximate mean of 0 and variance of
A gaussian distribution of (a).
4. The method of claim 3, wherein the removing the impulse noise data from the power line diversity signal received from the receiving end by the nonlinear function based on the optimal threshold estimation of noise samples comprises:
determining threshold T using a data sample based adaptive threshold estimation method t Using a zero-setting algorithm in a non-linear function to reduce the amplitude below a threshold T t Is replaced by zero, the reserved amplitude is greater than or equal to T t Of the noise signal
For the extracted noise time domain signal vector
M-th noise element of (1)
The zero-setting transformation is adopted:
where M represents the total number of samples in the tth transmission slot, |, represents the absolute value operation,
a nonlinear transformation result representing impulse noise at the mth sampling point of the tth transmission time slot;
order vector
Represent
Is a result of a non-linear transformation of
For power line signal
Eliminating impulse noise to obtain power line signal without impulse noise
Comprises the following steps:
like equations (5) and (6), F represents a discrete fourier transform operator.
5. The method of claim 4, further comprising:
will be provided with
With signals received at the wireless port
Merging to obtain y t Estimating symbol obtained by maximum likelihood detection algorithm for kth subcarrier of t-th transmission time slot
Is composed of
Wherein | · | purple 2 Which represents the 2-norm operation,
represents an equivalent received signal obtained by combining the power line signal and the wireless signal on the kth subcarrier of the t-th time slot, wherein omega represents a modulation signal constellation point set,
a symbol on a constellation diagram is represented,
which represents the equivalent channel coefficient at the kth subcarrier of the t transmission slot at the receiving end, N is the number of subcarriers,
meaning that the best x is chosen such that the function value f (x) for x is minimal.
6. The method of claim 5 wherein the threshold T is determined using a data sample-based adaptive threshold estimation method t The method comprises the following steps:
minimizing the error rate: let Pr (y) t (T t ) Means that the threshold value T is used in one OFDM data frame in the tth transmission slot t The obtained OFDM bit error rate is expressed as:
wherein | · | purple 0 Representing a 0-norm operation (i.e. counting the number of non-zero elements),
indicating the symbol sent by the sending end on the kth subcarrier of the tth transmission slot, where N is the number of subcarriers of one OFDM data frame, the objective function is:
wherein
Indicating a selected noise threshold
So as to relate to T t Error rate Pr (y) t (T t ) Minimum threshold, s.tT) t ≧ 0 indicates satisfaction of the "noise threshold T t The selection range of the receiving terminal is greater than or equal to 0 ″, and the receiving terminal obtains the signal at the t-th transmission time slot
And a channel coefficient matrix
And
based on the construction/updating of the real-time noise sample, the error rate problem at the current moment is converted into the average error rate problem of the minimized noise sample, and the optimal threshold value at the current moment is obtained
The expression is as follows:
wherein L is D Representing the magnitude of the number of noise samples, pr (y) i (T t ) Means that the ith OFDM data frame in the noise sample is related to the noise threshold T t S.t is an abbreviation of subject to, indicating that the constraint condition is satisfied;
when the threshold value is updated in the t-th transmission time slot, the weight lambda of each time slot in the data sample is calculated according to the discount factor gamma i ,i=1,2,…,L D According to the threshold value T at the T-1 th moment t-1 Combining the weights λ i For data sample D EN Calculating the error rate, and obtaining the threshold value T by a gradient descent method t-1 Corresponding gradient value of error rate
Using learning rate
Updating the threshold value at the current moment
Is shown in equation (16):
wherein
Denotes the value of x with respect to f (x) 0 The value of the gradient of (a) is,
is indicated at a noise threshold T t-1 Then, on the kth subcarrier of the ith OFDM data frame in the noise sample, combining the power line signal and the wireless signal to obtain an equivalent received signal,
indicating the symbol transmitted by the transmitting end on the k subcarrier of the ith OFDM data frame among the noise samples,
indicating the number of errors in the ith OFDM data frame in the noise sample,
then the weight of the ith OFDM data frame in the noise sample in the data sample is represented, i.e.
L D N represents the total number of symbols in the data sample, hence
Is the average bit error rate after weighted summation based on data samples.
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