CN115378467B - 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 transmitted in diversity in a power line and a wireless channel respectively, and the power line is affected by impulse noise; after diversity signals received by the receiving ends of the power line and the wireless channel are respectively subjected to discrete Fourier transform, noise data in the power line signals are obtained through offset operation; impulse noise data is removed from the power line signal received by the receiver by a nonlinear 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 aiming at a dual-interface communication architecture. The noise samples are extracted by utilizing the consistency of the power line and the wireless channel transmission signals, so that the limitation that impulse noise processing is carried out by only using the power line in traditional mixed communication is broken through, and the method can be applied to optimal threshold prediction.
Description
Technical Field
The invention relates to the technical field of smart 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 smart grid. The smart power grid has wide coverage range, complex topological structure and diversified access equipment, and a single communication technology cannot meet the requirements of various scenes, so that hybrid communication has become a key technology of the smart power grid. The power line communication (Power Line Communication, PLC) has wide coverage range and does not need additional wiring; the wireless communication (Wireless Communication, WLC) is flexible to access and high in expandability, so that the PLC and WLC technology are tightly combined, the system performance of hybrid communication is improved, and the wireless communication system has a wide application prospect in a smart grid and the Internet of things and becomes one of research hotspots.
However, the power line channel is extremely susceptible to Impulse Noise (IN) due to abrupt voltage changes caused by switching of the home appliances and the power electronics. Particularly, the burst impulse noise is generated at a plurality of continuous sampling points, so that the demodulation and decoding of signals are affected, and the synchronization performance of power line transmission is also affected. How to suppress impulse noise of a power line channel in dual-interface communication, and further improve coverage area of a communication system and reliability of access of the communication system are one of key problems to be solved.
Currently, in a dual-interface communication system under the influence of impulse noise in the prior art, researchers focus on technologies such as theoretical performance and signal combination. The Gamma distribution of wireless fading is approximately Log Normal (LogN) distribution, so that the mixed fading problem of a power line and a wireless parallel communication system is converted into the LogN-LogN same distribution fading problem, and then theoretical performances such as system error rate and the like are analyzed. The Middleton Class A impulse noise model is adopted, and the performance of algorithms such as maximum ratio combining, selective combining and the like in a power line and wireless parallel communication system is studied. However, these studies often only consider the impact of impulse noise on system performance and do not address how impulse noise is eliminated. Aiming at different types of impulse noise and narrow-band interference, a multi-stage orthogonal matching pursuit recovery algorithm is provided for jointly recovering the impulse noise and the narrow-band interference, but the complexity of parallel processing of the dual-interface signals is higher.
Compared with a compressed sensing algorithm, the impulse noise suppression algorithm based on the nonlinear function has the advantages of high calculation speed, no need of empty subcarriers and the like, and the attention of researchers is obtained. Where the most widely used nonlinear functions include zero setting, clipping/deep clipping, and combinations thereof. In an orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) system, however, the presence of Peak-to-Average Power Ratio (PAPR) results in a portion of the useful signal being treated as noise if an accurate noise threshold is not obtained, which greatly limits the performance of the nonlinear function.
In recent years, the determination of an optimal threshold for a nonlinear function has been increasingly attracting attention. The method comprises the steps of discretizing burst impulse noise through an interleaving technology, obtaining noise statistical parameters by utilizing moment estimation and calculating a noise threshold, wherein the scheme ignores the influence of channel fading on a system, and the interleaving technology increases the transmission delay of the system. The application of the algorithm in a delay-sensitive network is not favored. For deterministic signals in impulse noise, the local optimum detection (Locally Optimal Detection, LOD) algorithm performs best at low signal-to-noise ratios, approaching the theoretical optimum solution. The LOD algorithm requires accurate knowledge of the probability density function (Probability Density Function, PDF) of the noise, but may not be available in a real scene. There are schemes to combine clipping with time/frequency channel equalization, where combined clipping is performed before OFDM signal demodulation, and then time/frequency equalization is performed, but the improvement of the communication quality by this method is not obvious, and the threshold is typically obtained empirically.
The solutions in the prior art are to process impulse noise in the PLC alone, and do not relate to a dual-interface communication environment of power line and wireless.
Disclosure of Invention
The embodiment of the invention provides a power line noise sample extraction method based on diversity signal cancellation, which is used for effectively extracting noise samples of diversity signals in a power line channel.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A power line noise sample extraction method based on diversity signal cancellation, comprising:
diversity signals to be transmitted are transmitted in diversity in a power line and a wireless channel, respectively, wherein the power line is influenced by impulse noise;
after the diversity signals received by the power line channel and the receiving end of the wireless channel are respectively subjected to discrete Fourier transform, noise data in the power line diversity signals are obtained through offset operation;
the impulse noise data is removed from the received power line diversity signal by a nonlinear function based on an optimal threshold estimate of the noise samples.
Preferably, the diversity signal to be transmitted is transmitted in diversity in a power line and a wireless channel, respectively, the power line is affected by impulse noise, and the method comprises:
diversity signals to be transmittedDiversity transmission is carried out in a power line and a wireless channel respectively, the wireless channel is influenced by Rayleigh fading and Gaussian white noise, the power line channel is influenced by log-normal fading and impulse noise, and the diversity signals are subjected to Orthogonal Frequency Division Multiplexing (OFDM) modulation in the power line and the wireless communication module respectively;
wireless channel fading coefficient h W Satisfying Rayleigh distribution, h W The probability density function PDF of (2) is:
wherein the method comprises the steps ofIs the rayleigh fading variance;
fading coefficient h of power line channel P Satisfying the LogN distribution, h P The probability density function PDF of (2) is:
mu in the middle P Andlnh respectively P Is normalized to the mean and variance of the channel fading energy to obtain +.>
Preferably, after performing discrete fourier transform on the diversity signals received by the receiving ends of the power line channel and the wireless channel, respectively, pulse noise data in the power line diversity signals is obtained through cancellation operation, including:
OFDM modulated transmission time slot tTime domain symbol isDiversity signal to be transmitted by the transmitting end->The fading coefficient of the channel is +.>Is +.>Diversity transmission in the radio channel of (a), power line channel and time domain OFDM sampling signal received by the receiving end of the radio channel +.>And->Expressed as:
wherein the symbols areRepresenting convolution operation; />And->Respectively representing time domain Gaussian noise vectors on a power line and a wireless channel, wherein elements in the vectors respectively meet the conditions that the mean value is zero and the variance is +>And->Is a gaussian distribution of (c); />Representing a time domain impulse noise vector whose elements satisfy a mean of zero and a variance of +.>Gaussian distribution of (2), and->Greater than 1;
will beAnd->After removing the cyclic prefix, performing discrete Fourier transform DFT to obtain equivalent frequency domain OFDM signals of the power line channel and the wireless channel->And->Expressed as:
where F represents the DFT operator and,representing the OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N being the number of subcarriers, [] T Representing transpose operations->And->Equivalent channel coefficient matrix respectively representing t-th transmission time slot power line and wireless channel,/or%>And->Are diagonal matrices and +.>Reversible, let->The left-hand matrix Q may be:
wherein the method comprises the steps of Representation->Subtracting the equation (5) from the equation (7) to obtain the same signal part (I) of the power line channel receiver which is counteracted with the wireless channel>Post frequency domain noise estimate vector ψ t The method comprises the following steps:
wherein the method comprises the steps ofFrequency domain vector after DFT conversion for time domain impulse noise,>a background noise interference term representing a dual interface channel;
let F * Representing an inverse discrete Fourier transform operator, then pair ψ t Performing an inverse discrete Fourier transform F * Ψ t Obtaining a time domain noise signal vectorThe method comprises the following steps:
wherein the method comprises the steps ofAnd->Obeying a gaussian distribution with an average value of 0, will +.>Approximately to mean 0, variance +.>Is a gaussian distribution of (c).
Preferably, the canceling the impulse noise data from the power line diversity signal received by the receiving end by the nonlinear function based on the optimal threshold estimate of the noise samples comprises:
determining a threshold T by adopting a self-adaptive threshold estimation method based on data samples t The amplitude is smaller than the threshold T by adopting a zero setting algorithm in a nonlinear function t Is replaced by zero, the reserved amplitude is greater than or equal to T t Is a noise signal of (a)For the extracted noise time domain signal vector +.>The mth noise element +.>The zero-setting transformation is adopted as follows:
where M represents the total number of sampling points for the t-th transmission slot, |·| represents the absolute value operation,representing the nonlinear transformation result of impulse noise at the mth sampling point of the t-th transmission time slot;
let vectorRepresentation->Is the nonlinear transformation result of->For power line signal->Performing impulse noise elimination to obtain power line signal without impulse noiseNumber->The method comprises the following steps:
like equations (5) and (6), F represents a discrete fourier transform operator.
Preferably, the method further comprises:
will beSignal received from wireless port->Carrying out combination treatment to obtain->Estimated symbol +.f. obtained by maximum likelihood detection algorithm for kth subcarrier of t-th transmission slot>Is that
Wherein I II 2 Representing a 2-norm operation,representing the equivalent received signal after combining the power line signal and the wireless signal on the kth subcarrier of the nth time slot, wherein Ω represents a modulated signal constellation point set, +.>Representing symbols on the constellation, < >>Representing the equivalent channel coefficient of the receiving end on the kth subcarrier of the t transmission time slot, N is the number of subcarriers,the representation selects the best x such that the function value f (x) for x is the smallest.
Preferably, the threshold T is determined by using an adaptive threshold estimation method based on data samples t Comprising:
minimizing bit error rate: let Pr (y) t (T t ) Indicating the use of a threshold T in an OFDM data frame in the T-th transmission slot t The obtained OFDM error rate is expressed as:
wherein I II 0 Representing a 0-norm operation (i.e. counting the number of non-zero elements),representing the symbol sent by the sender on the kth subcarrier of the t transmission time slot, N is the number of subcarriers of one OFDM data frame, the objective function is:
wherein the method comprises the steps ofRepresenting the selected noise threshold ∈ ->So that about T t Is set to be the bit error rate Pr (y) t (T t ) A minimum threshold, s.t T t 0 means that "noise threshold T" is satisfied t The condition requirement that the selection range of the signal is more than or equal to 0' is that the receiving end obtains the signal in the t-th transmission time slot>Channel coefficient matrix->And->Based on the construction/updating of the real-time noise sample, the problem of minimizing the bit error rate at the current moment is converted into the problem of minimizing the average bit error rate of the noise sample, and the optimal threshold value at the current moment is obtainedThe expression is:
wherein L is D Represents the number of noise samples, pr (y i (T t ) I) represents that the ith OFDM data frame in the noise samples is with respect to the noise threshold T t S.t is an abbreviation for subject to, indicating that the constraint is satisfied;
when the t-th transmission time slot is updated in threshold value, 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 T at time T-1 t-1 Combining the weights lambda i For data sample D EN Calculating error rate, and obtaining threshold T by gradient descent method t-1 Gradient value of corresponding error rateUsing learning rate l R Updating threshold value +.> Is calculated by (a)The solution process is shown in the formula (16):
wherein the method comprises the steps ofRepresenting the relation f (x) at x 0 Gradient values at>Expressed at noise threshold T t-1 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 receiving signal, < >>Representing the symbol transmitted by the transmitting end on the kth subcarrier of the ith OFDM data frame in the noise samples,representing the number of errors of the ith OFDM data frame in noise samples,/and/or>Then the weight of the ith OFDM data frame in the noise samples, i.e./in the data samples>L D N represents the total number of symbols in the data sample, thus +.>Is the average bit error rate based on the weighted summation of the data samples.
The technical scheme provided by the embodiment of the invention can be seen that the method of the invention provides a power line noise sample extraction method based on diversity signal cancellation aiming at a dual-interface communication architecture. Noise samples are extracted by utilizing the consistency of the transmission signals of the power line channel and the wireless channel, so that the limitation that impulse noise processing is carried out by only using the power line in traditional mixed communication is broken through, and the method can be applied to optimal threshold prediction.
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 required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a process flow diagram 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 of a Markov chain representation of a TSMG noise model according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a frame of a signal processing performed at a receiving end by a power line and wireless dual-interface communication system according to an embodiment of the present invention;
FIG. 4 is a probability density of an embodiment of the present inventionIs a statistical histogram of (1);
fig. 5 is a diagram showing a noise data extraction effect based on dual-interface diversity signal cancellation according to an embodiment of the present invention; fig. 5 (a) a waveform diagram of a power line channel time domain signal, fig. 5 (b) a waveform diagram of a wireless channel time domain signal, and fig. 5 (c) a comparison of the extracted signal with an actual IN;
FIG. 6 shows Bit Error Rate (BER) and eta under the same signal-to-noise ratio WCC And eta SC Schematic diagram of variation with threshold T.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be 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 expressly stated otherwise, as understood by those skilled in the art. 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. The term "and/or" as used herein 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 purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
In view of the analysis of dual interface communications and nonlinear functions. The invention provides a pulse noise suppression method based on double-interface diversity signal cancellation and self-adaptive threshold estimation (Diversity Signal Cancellation and Adaptive Threshold Estimation, DSC-ATE) according to independence and difference of power lines and wireless channels and consistency of diversity signals. The method comprises the steps of firstly extracting noise samples by utilizing a wireless channel auxiliary power line channel, then adaptively estimating impulse noise threshold values through the noise samples, and finally separating impulse noise from a received signal.
The invention designs an algorithm based on diversity signal cancellation and adaptive threshold estimation, which is used for suppressing burst impulse noise in a power line channel in double-interface communication. First, in consideration of the consistency of diversity signals, a power line channel is assisted by a wireless channel for preprocessing, and noise samples are extracted. In order to accurately estimate the noise threshold, the noise threshold is iteratively estimated using data samples and an objective function minimization/maximization algorithm to determine the location information of the impulse noise. Simulation results show that the method can effectively improve the suppression capability of burst impulse noise and the reliability of a communication system. The algorithm can be researched by combining the algorithm with sparse theory, compressed sensing and the like aiming at other noise models such as narrow-band noise, mixed noise and the like so as to research the elimination performance of the algorithm on other noise.
A dual-interface OFDM (Orthogonal Frequency Division Multiplexing ) communication system model employed by the present invention is shown in fig. 1. The transmitted signals are subjected to OFDM modulation in the power line and the wireless communication module, respectively, and are transmitted in diversity in the power line and the wireless channel. Wherein the wireless channel is affected by rayleigh fading and gaussian white noise and the power line channel is affected by log normal fading and impulse noise. The power line signal and the wireless signal at the receiving end simultaneously contain the same transmitting information, and only the power line signal has impulse noise with larger amplitude, so that the signal after discrete Fourier transform (Discrete Fourier Transform, DFT) can be subjected to cancellation operation to obtain impulse noise data in the power line signal. The impulse noise data can be obtained with the assistance of diversity signals transmitted by the wireless channel. And then combining the power line with the wireless signal and decoding and outputting the combined signal. The extracted impulse noise data is stored, so that a data sample space can be established, and data support is provided for the algorithm or other algorithms.
Wireless channel fading coefficient h W Satisfying Rayleigh distribution, h W The probability density function PDF of (2) is:
wherein the method comprises the steps ofIs the variance of rayleigh fading.
Fading coefficient h of power line channel P Satisfying the LogN distribution, h P Is the probability density function PDF of (2)
Mu in the middle P Andlnh respectively P Mean and variance of (c). Normalizing the channel fading energy to obtain
Based on the correlation of burst noise in time, the invention establishes a Two-State Markov-Gaussian (TSMG) model. Fig. 2 is a schematic diagram of a markov chain representation 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 represented by a noise state s k E { B, I }, where B represents that the channel is only disturbed by background noise and I represents that the channel is affected by impulse noise. The noise state can be generated by using a { s }, a 1 ,s 2 ,…,s K Sequence characterization of }. For this model, the (K+1) th noise state s K+1 Can use first order MarkThe koff process represents:
wherein s is k Sum s k+1 Respectively represent noise states of sampling points at different times, P (s K+1 ) Represents the (K+1) th sampling point, the noise is in the state s K+1 Probability values of (a) are provided. s is(s) 1 Representing an initial noise state, without loss of generality, an initial noise state s 1 Randomly choose between background noise states and impulse noise states.
Thus, the state change process of noise can be represented by the state transition probability Ps k s k+1 ＝P(s k+1 |s k ) And (3) representing. According to the state transition probability, the probability P of the noise in the state I and the state B is obtained respectively I And P B Is that
Wherein P is BI Representing the transition probability of state B to state I, P IB Is the transition probability of state I to state B. s is(s) k The =i represents the noise state s at the kth state (sampling point) k Is a pulse noise state.
s k The symbol =b represents the noise state s in the kth state (sampling point) k Is a background noise state. Usable parametersTo describe the size of the noise memory, +.>Indicating that the noise is memory-free, but +.>Indicating that the noise has a memory, i.e. there is a temporal correlation.
In the following description, the power line channel is subject to background noiseIs also subject to impulse noise +.>Requires the Markov sequence { s } in the TSMG model 1 ,s 2 ,…,s K Characterization, whereas 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 signal processing at a receiving end according to an embodiment of the present invention. Firstly, removing a power line and a wireless time domain signal Cyclic Prefix (CP) at a receiving end, and carrying out ideal channel estimation by utilizing a pilot signal; because the two channels transmit the same information, under ideal channel estimation, the data part which is the same as the wireless signal in the power line signal can be removed through offset operation, so that noise samples in the power line signal are reserved; extracting impulse noise in the noise sample through a nonlinear function, and eliminating the impulse noise from the power line signal; and finally, combining the diversity signals in the power line and the wireless channel, and decoding and outputting.
The OFDM modulated time domain symbol of the t th transmission time slot isThe channel fading coefficient is +.>And->After the same diversity signal is transmitted by the power line and the wireless parallel channel, the receiving end receives the time domain OFDM sampling signal +.>And->Can be expressed as:
wherein the symbols areRepresenting convolution operation, ++>And->Time domain Gaussian noise vectors on a power line and a wireless channel are respectively represented, elements in the vectors respectively meet the conditions that the mean value is zero, and the variance is +.>And->Is a gaussian distribution of (c); />Representing time domain impulse noise vectors, elements thereofThe element satisfies the mean value of zero and the variance of +.>Gaussian distribution of (2), and->Greater than 1. After removing CP from the OFDM signal and performing DFT, equivalent frequency domain OFDM signal +.>And->Expressed as:
where F represents the DFT operator and,representing the OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N being the number of subcarriers, [] T Representing a transpose operation. />And->The present invention ignores the influence of inter-code crosstalk because the CP is utilized to combat frequency selective fading caused by multipath channels, so +_>And->Are diagonal matrices and +.>And is reversible. Let->The left-hand matrix Q is available
Wherein the method comprises the steps of Representation->Is a matrix of inverse of (a). Subtracting the equation (8) from the equation (10) to obtain the same signal part of the power line channel receiver which is cancelled out of the wireless channel>Then, the obtained frequency domain noise estimation vector ψ t The method comprises the following steps:
wherein the method comprises the steps ofFrequency domain vector after DFT conversion for time domain impulse noise,>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 noise time domain signal vectorThe method comprises the following steps:
wherein,and->Obeying a gaussian distribution with an average value of 0. Under the condition of known Q +.>Approximately mean 0, variance +.>Is a gaussian distribution of (c). The present invention performs statistical analysis on noise data samples, and FIG. 4 shows a probability density according to an embodiment of the present invention>Statistical histogram of (2), and->Distribution and Gaussian distribution ∈>And the heights are coincident.
Fig. 5 shows a noise data extraction effect based on dual-interface diversity signal cancellation according to an embodiment of the present invention, wherein the signal-to-noise ratio (Signal to Noise Ratio, SNR) S NR =10 dB, other system parameters are shown in table 1. Fig. 5 (a), 5 (b) are time domain signals received by the power line and the wireless channel, respectively, and fig. 5 (c) shows the processed signalsComparison with the actual impulse noise. As can be seen from fig. 5 (a) and 5 (b), the presence of PAPR in the OFDM system makes it difficult to directly acquire the location information of impulse noise in the power line channel. After diversity signal cancellation, as shown in FIG. 5 (c), due to +.>Only background interference remains, and the influence of the PAPR is very small, so that nonlinear transformation can be used to obtain the position information of impulse noise. It is apparent that the noise data in fig. 5 (c) can be preserved, providing large 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 less than the threshold T 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 efficient. Thus for the extracted noise time domain signal vectorThe mth noise element in (a)The zero-setting transformation is adopted as follows:
where M represents the total number of sampling points for the t-th transmission slot, |·| represents the absolute value operation,representing the nonlinear transformation result of impulse noise at the mth sampling point of the t-th transmission slot.
Let vectorRepresentation->Is the nonlinear transformation result of->For power line signal->Pulse noise cancellation is performed to obtain a pulse noise-free power line signal +.>The method comprises the following steps:
like equations (8) and (9), F represents a discrete fourier transform operator.
The power line signal after removing impulse noiseAnd wireless signal->Combining to obtain y t Finally, the estimated symbol (I) obtained by adopting maximum likelihood detection algorithm to the kth subcarrier of the tth transmission time slot is adopted>The method comprises the following steps:
wherein I II 2 Representing a 2-norm operation,representing the equivalent received signal after combining the power line signal and the wireless signal on the kth subcarrier of the nth time slot, wherein Ω represents a modulated signal constellation point set, +.>Representing symbols on the constellation, < >>Representing the equivalent channel coefficient of the receiving end on the kth subcarrier of the t transmission time slot, N is the number of subcarriers,the representation selects the best x such that the function value f (x) for x is the smallest.
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 critical to algorithm performance, where the traditional weighted combination criterion (Weighted Combination Criterion, WCC) and siebert criterion (Siegert Criterion, SC) obtain the best threshold by balancing the detection probability of impulse noise with the false alarm probability. Extensive research has been achieved because of the fewer parameters required for WCC and SC.
Definition P a P is a good detection probability of impulse noise b Is false alarm probability, wherein the comprehensive objective function is eta WCC ＝P a -P b The optimal threshold T of WCC criterion WCC The method comprises the following steps:
as shown in the formula (16), the key step of the WCC criterion is to target the comprehensive function of the detection probability and the false alarm probability to determine the optimal threshold T WCC From the formula (16), η is found WCC The larger the false alarm probability and the miss probability are, the smaller. At a power of known impulse noisePower of background interference->Under the condition of (2), P can be given separately a And P b Then the objective function eta is calculated WCC The threshold T is biased to obtain the optimal threshold T WCC The method comprises the following steps:
similarly, let the objective function eta SC ＝P I P a +P B (1-P b ) The best threshold T of the SC criterion is obtained SC 。
FIG. 6 shows Bit Error Rate (BER) and eta under different signal-to-noise ratios according to an embodiment of the present invention WCC And eta SC Schematic diagram of variation with threshold T. It can be seen that the objective of BER minimization is the presence of an optimal threshold T * But the optimal threshold T * And a threshold T obtained by two criteria WCC 、T SC There is a certain deviation. This is because both criteria solve the threshold for the composite target with the detection probability and the false alarm probability, rather than directly using BER as the optimization target.
Adaptive threshold estimation based on data samples
Conventional threshold estimation algorithms require accurate noise parameters. Because of the non-stationarity of the communication environment, the parameter acquisition is difficult, and a certain deviation exists between the threshold value acquired by the traditional algorithm and the true optimal solution.
Inspired by data-driven machine learning and transfer learning, obtaining the optimal threshold of the nonlinear function through noise samples and gradient descent is a new idea of 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 that the aim of directly optimizing the objective function and reducing deviation is fulfilled. Because of the lack of a disclosed impulse noise database, the diversity cancellation algorithm proposed by the present invention can be used to construct a noise sample database and for optimal threshold estimation. In order to ensure the real-time performance of the algorithm, the latest noise samples are used for carrying out iterative updating of the threshold value so as to cope with the change of the environment or the parameter.
The best threshold estimation algorithm is closely related to the selection of the objective function, and specific objective functions need to be established for different communication scenes and performance indexes. The invention mainly provides the following two forms:
1) Minimizing BER: in OFDM communication systems, the use of BER evaluation algorithms is most common in performance, which is also one of the indicators in quality of service (Quality of Service, qoS). The symbol obtained by the decoding judgment of the formula (15) is compared with the symbol sent by the sending end, thereby obtaining the error rate and enabling Pr (y) t (T t ) Indicating the use of the threshold T at time T t The bit error rate of one OFDM frame obtained can be expressed as:
wherein the method comprises the steps ofExpressed at noise threshold T t On the kth subcarrier of the next t transmission time slot, the equivalent received signal after the power line signal and the wireless signal are combined is I 2 Representing a 2-norm operation, I.I 0 Representing 0-norm operations (i.e. counting the number of non-zero elements), Ω represents a set of modulated signal constellation points, +.>Representing symbols on the constellation, < >>Representing the equivalent channel coefficient of the receiving end on the kth subcarrier of the t-th transmission time slot,/for the receiving end>Representing the symbol transmitted by the transmitting end on the kth subcarrier of the tth transmission slot. Similarly N is the number of subcarriers of one OFDM data frame, +.>The representation selects the best x such that the function value f (x) for x is the smallest. The objective function is:
wherein Pr (y) t (T t ) A) represents the use of a threshold T for one OFDM frame in the T-th transmission slot t The resulting OFDM error rate is used to determine,then represent the selected noise threshold +.>So that about T t Is set to be the bit error rate Pr (y) t (T t ) A minimum threshold.
2) Maximizing transmission rate R t : the transmission rate and channel quality are directly related. After normalizing the channel bandwidth, the equivalent signal to noise ratio can be improved by eliminating 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 limited length coding, which is at the noise threshold T t Lower average transmission rate R t (T t ) Can be expressed as
Wherein the method comprises the steps ofIndicating that the receiving end is at the noise thresholdT t Equivalent signal-to-noise ratio of kth subcarrier of next t transmission time slot, V represents channel dispersion, L represents coding length, Q -1 (. Cndot.) is the complementary error function, and ε represents the bit error rate, the objective function is
Wherein the method comprises the steps ofThe representation selects the best x such that the function value f (x) for x is the largest.
The characteristics of different objective functions are similar, and a similar optimization design method can be adopted.
At the t-th transmission time slot, only the signal is obtained by the receiving endChannel coefficient matrix->Andtherefore, the error rate of the t-th transmission time slot cannot be directly obtained, and the threshold optimization cannot be further performed. Based on the construction/update of the real-time noise sample, the invention converts the problem of minimizing the error rate at the current moment into the problem of minimizing the error rate of the noise sample, thereby obtaining the optimal threshold value +.>The expression is
Wherein L is D Representing the magnitude of the number of noise samples. Random letterUnder the road condition, L D The choice of (c) requires a compromise in terms of 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 . Then based on the threshold T of the previous time slot (i.e. the T-1 th time slot) t-1 Combining the weights lambda i For data sample D EN The error rate is calculated, and then a threshold T is obtained through a gradient descent method t-1 Corresponding error rate gradient valueFinally, the learning rate l is used R Updating threshold value +.> 1 ). T is described in the appendix t The result of the solving process of (2) is shown as the formula (23)
Wherein the method comprises the steps ofRepresenting the relation f (x) at x 0 Gradient values at>Expressed at noise threshold T t-1 And combining the power line signal and the wireless signal to obtain an equivalent received signal on the kth subcarrier of the ith OFDM data frame in the noise sample. />Representing the symbol transmitted by the transmitting end on the kth subcarrier of the ith OFDM data frame in the noise sample.Representing errors of an ith OFDM data frame in noise samplesNumber of codes. />Then the weight of the ith OFDM data frame in the noise samples, i.e./in the data samples>L D N represents the total number of symbols in the data sample, thus +.>Is the average bit error rate based on the weighted summation of the data samples.
In addition, the invention adopts a queue storage mode (first-in first-out rule) to save/update the noise samples, thereby ensuring the effectiveness of noise data. The specific steps of this algorithm are as follows:
through the steps, a more accurate threshold value can be obtained. Reconstructing impulse noise by (13)And subtracting the reconstructed impulse noise from the frequency domain OFDM signal before demodulation through 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 the data symbol.
The characteristics of the algorithm of the invention are analyzed in terms of computational complexity, threshold accuracy, update rate and the like.
1) Complexity analysis: in the process of aiming at minimizing BER, the complexity of the algorithm mainly relates to three operations of judgment, addition and subtraction and multiplication. Wherein the judgment and addition and subtraction are the linear operation with the fastest operation efficiency, mainly comprising three parts of nonlinear transformation of formula (13), (14) impulse noise removal and (18) decoding judgment, and the multiplication mainly comprisesDFT, etc. Through analysis, in one iteration calculation, the complexity of the linear operand and the multiplication operation is O (kN).
2) Threshold accuracy: the noise sample is directly derived from the physical environment where the communication is located, so that the communication quality at the current moment can be accurately reflected, and a more accurate threshold value can be obtained on the premise that a noise model and parameters are not needed. The traditional algorithm needs to obtain statistical data of the environment and a related model, and a phenomenon that the actual environment is not matched with the model can occur.
3) Update rate: when the environment changes, it is necessary to quickly adapt to the dynamic changes of the channel or environment. Through the learning rate and the discount factor, the DSC-ATE algorithm can dynamically adjust the threshold according to environmental changes, and the deviation from the actual threshold is reduced. While the traditional algorithm is constrained by a statistical model, when model parameters are unchanged, the threshold cannot be quickly adjusted.
In summary, compared with the existing method, the embodiment of the invention has the following main contributions:
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 samples are extracted by utilizing the consistency of the power line and the wireless transmission diversity signals, so that the limitation that impulse noise processing is carried out by only using the power line in traditional mixed communication is broken through, and the method can be applied to optimal threshold prediction.
2) Based on diversity transmission, a sample space of impulse noise is constructed, which contains noise samples at low signal-to-noise ratio (abnormal communication); and by combining a nonlinear function, an optimal threshold estimation algorithm based on noise samples is provided, so that the minimum error rate of the communication system can be realized, and priori information of impulse noise is not needed.
3) For the problem of real-time change of the optimal threshold in the non-stationary environment, the algorithm can better utilize noise samples by introducing parameters such as learning rate, discount factor and the like to adjust the convergence rate of the algorithm, so that the robustness is improved, and the effective compromise of the algorithm in the robustness and the convergence rate is realized.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be 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 (3)
1. A method for extracting power line noise samples based on diversity signal cancellation, comprising:
diversity signals to be transmitted are transmitted in diversity in a power line and a wireless channel, respectively, wherein the power line is influenced by impulse noise;
after the diversity signals received by the power line channel and the receiving end of the wireless channel are respectively subjected to discrete Fourier transform, noise data in the power line diversity signals are obtained through offset operation;
removing the impulse noise data from the received power line diversity signal by a nonlinear function based on an optimal threshold estimate of the noise samples;
after the diversity signals received by the power line channel and the receiving end of the wireless channel are respectively subjected to discrete Fourier transform, impulse noise data in the power line diversity signals are obtained through cancellation operation, and the method comprises the following steps:
the OFDM modulated time domain symbol of the t th transmission time slot isDiversity signal to be transmitted by the transmitting end->The fading coefficient of the channel is +.>Is +.>Diversity transmission in the radio channel of (a), power line channel and time domain OFDM sampling signal received by the receiving end of the radio channel +.>And->Expressed as:
wherein the symbols areRepresenting convolution operation; />And->Respectively representing time domain Gaussian noise vectors on a power line and a wireless channel, wherein elements in the vectors respectively meet the conditions that the mean value is zero and the variance is +>And->Is a gaussian distribution of (c); />Representing a time domain impulse noise vector whose elements satisfy a mean of zero and a variance of +.>Gaussian distribution of (2), and->Greater than 1;
will beAnd->After removing the cyclic prefix, performing discrete Fourier transform DFT to obtain equivalent frequency domain OFDM signals of the power line channel and the wireless channel->And->Expressed as:
where F represents the DFT operator and,representing the OFDM frequency domain symbol vector transmitted by the transmitting end in the t-th transmission slot, N being the number of subcarriers, [] T Representing transpose operations->And->Equivalent channel coefficient matrix respectively representing t-th transmission time slot power line and wireless channel,/or%>And->Are diagonal matrices and +.>Reversible, let->The left-hand matrix Q may be:
wherein the method comprises the steps ofRepresentation->Subtracting the equation (5) from the equation (7) to obtain the same signal part (I) of the power line channel receiver which is counteracted with the wireless channel>Post frequency domain noise estimate vector ψ t The method comprises the following steps:
wherein the method comprises the steps ofFrequency domain vector after DFT conversion for time domain impulse noise,>a background noise interference term representing a dual interface channel;
let F * Representing an inverse discrete Fourier transform operator, then pair ψ t Performing an inverse discrete Fourier transform F * Ψ t Obtaining a time domain noise signal vectorThe method comprises the following steps:
wherein the method comprises the steps ofAnd->Obeying a gaussian distribution with an average value of 0, will +.>Approximately with a mean of 0 and a variance of sigma G 2 Is a gaussian distribution of (c);
the cancellation of the impulse noise data from the received power line diversity signal by the nonlinear function based on the optimal threshold estimate of the noise samples, comprising:
determining a threshold T by adopting a self-adaptive threshold estimation method based on data samples t The amplitude is smaller than the threshold T by adopting a zero setting algorithm in a nonlinear function t Is replaced by zero, the reserved amplitude is greater than or equal to T t Is a noise signal of (a)For the extracted noise time domain signal vector xi t The mth noise element +.>The zero-setting transformation is adopted as follows:
where M represents the total number of sampling points for the t-th transmission slot, |·| represents the absolute value operation,representing the nonlinear transformation result of impulse noise at the mth sampling point of the t-th transmission time slot;
let vectorRepresentation->Is the nonlinear transformation result of->For power line signal->Performing impulse noise cancellation to obtain a power line signal without impulse noise>The method comprises the following steps:
like equations (5) and (6), F represents a discrete Fourier transform operator;
the self-adaptive threshold estimation method based on the data samples is adopted to determine the threshold T t Comprising:
minimizing bit error rate: let Pr (y) t (T t ) Indicating the use of a threshold T in an OFDM data frame in the T-th transmission slot t The obtained OFDM error rate is expressed as:
wherein I II 0 Representing the operation of the 0-norm,representing the symbol sent by the sender on the kth subcarrier of the t transmission time slot, N is the number of subcarriers of one OFDM data frame, the objective function is:
wherein T is t * Representing the selected noise threshold T t * So that about T t Is set to be the bit error rate Pr (y) t (T t ) A minimum threshold, s.t T t 0 means that "noise threshold T" is satisfied t The condition requirement that the selection range is more than or equal to 0' is that at the t-th transmission time slot, the receiving end obtains the signalChannel coefficient matrix->And->Based on the construction/updating of the real-time noise sample, the problem of minimizing the bit error rate at the current moment is converted into the problem of minimizing the average bit error rate of the noise sample, and the optimal threshold value of the current moment is obtained>The expression is:
wherein L is D Represents the number of noise samples, pr (y i (T t ) I) represents that the ith OFDM data frame in the noise samples is with respect to the noise threshold T t S.t is an abbreviation for subject to, indicating that the constraint is satisfied;
when the t-th transmission time slot is updated in threshold value, 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 T at time T-1 t-1 Combining the weights lambda i For data sample D EN Calculating error rate, and obtaining threshold T by gradient descent method t-1 Gradient value of corresponding error rateUsing learning rate l R Updating a threshold value at a current time The solving process of (2) is shown as the formula (16):
wherein the method comprises the steps ofRepresenting the relation f (x) at x 0 Gradient values at>Expressed at noise threshold T t-1 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 receiving signal, < >>Representing the symbol transmitted by the transmitting end on the kth subcarrier of the ith OFDM data frame in the noise samples, a>Representing the number of errors of the ith OFDM data frame in noise samples,/and/or>Then the weight of the ith OFDM data frame in the noise samples, i.e./in the data samples>L D X N represents the total number of symbols in the data sample, and thereforeIs the average bit error rate based on the weighted summation of the data samples.
2. The method of claim 1, wherein the diversity signals to be transmitted are transmitted diversity in a power line and a wireless channel, respectively, the power line being affected by impulse noise, comprising:
diversity signals to be transmittedDiversity transmission is carried out in a power line and a wireless channel respectively, the wireless channel is influenced by Rayleigh fading and Gaussian white noise, the power line channel is influenced by log-normal fading and impulse noise, and the diversity signals are subjected to Orthogonal Frequency Division Multiplexing (OFDM) modulation in the power line and the wireless communication module respectively;
wireless channel fading coefficient h W Satisfying Rayleigh distribution, h W The probability density function PDF of (2) is:
wherein the method comprises the steps ofIs the rayleigh fading variance;
fading coefficient h of power line channel P Satisfying the LogN distribution, h P The probability density function PDF of (2) is:
mu in the middle P Andlnh respectively P Normalized by the mean and variance of the channel fading energy to obtainTo->
3. The method of claim 1, wherein the method further comprises:
will beSignal received from wireless port->Combining to obtain y t Estimated symbol +.A maximum likelihood detection algorithm is used for kth subcarrier of the t-th transmission slot>Is that
Wherein I II 2 Representing a 2-norm operation,representing the equivalent received signal after combining the power line signal and the wireless signal on the kth subcarrier of the nth time slot, wherein Ω represents a modulated signal constellation point set, +.>Representing the symbols on the constellation diagram,representing the equivalent channel coefficient of the receiving end on the kth subcarrier of the t transmission time slot, N is the number of subcarriers,representing that the best x is chosen such that the function value f (x) for x is the mostIs small.
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