CN112003801B - Channel impulse response and impulse noise joint estimation method, system and equipment - Google Patents

Channel impulse response and impulse noise joint estimation method, system and equipment Download PDF

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CN112003801B
CN112003801B CN202010864920.3A CN202010864920A CN112003801B CN 112003801 B CN112003801 B CN 112003801B CN 202010864920 A CN202010864920 A CN 202010864920A CN 112003801 B CN112003801 B CN 112003801B
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plc
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CN112003801A (en
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赵闻
张捷
黄友朋
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Measurement Center of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/46—Monitoring; Testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H04B3/544—Setting up communications; Call and signalling arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0264—Arrangements for coupling to transmission lines
    • H04L25/0292—Arrangements specific to the receiver end

Abstract

The invention discloses a channel impulse response and impulse noise joint estimation method, a system and equipment, wherein a MIMO-PLC channel model and a noise model are established to respectively solve the channel frequency response and the noise of each receiving end of the MIMO-PLC channel, and the MIMO-PLC channel receiving signal is calculated on the basis; by utilizing the correlation of a channel and the sparse characteristics of channel impact response and impulse noise, the structure and space distribution information of a physical signal can be effectively mined by utilizing block sparsity based on a fast block sparse Bayesian learning (BSBL-FM) method, so that the performance of a sparse reconstruction algorithm is remarkably improved.

Description

Channel impulse response and impulse noise joint estimation method, system and equipment
Technical Field
The present invention relates to the field of communications, and in particular, to a method, a system, and a device for jointly estimating channel impulse response and impulse noise.
Background
Power Line Communication (PLC) is a technology for transmitting data signals using a power line. Because the power line system has the advantages of wide line distribution, low installation cost and the like, the PLC is widely regarded as an important communication mode in the fields of smart power grids, home automation, office automation and the like. Generally, according to the voltage class transmitted by the power line, PLCs can be classified into three categories: low voltage power line communication (commonly referred to as 220V/380V voltage class); medium voltage power line communication (typically referred to as 10kV voltage class); high voltage power line communications (typically referring to voltage levels of 35kV and above). With the increase of communication requirements of smart power grids and smart home construction, high-speed and broadband PLC becomes the main trend of current development. In order to accelerate the development of the PLC, a multiple-input multiple-output (MIMO) technology is introduced into the low-voltage PLC, so that further improvement of the system performance can be effectively realized. MIMO-PLC systems utilize multiple transmission channels, which can provide greater channel capacity and higher data rates. The accuracy of Channel State Information (CSI) will directly affect the overall performance of the MIMO-PLC system, and therefore an accurate channel estimation technique is a key to ensure the communication quality.
Noise is also one of the main factors affecting communication quality, and the most typical characteristic of the low-voltage PLC network as a power supply private network is that a large number of electrical devices are connected to the terminals thereof, and interference generated by the operation of the electrical devices directly acts on a PLC channel, which is one of the main noise sources in the PLC communication system. The PLC channel noise exhibits overall non-gaussian, non-stationary characteristics, and the classical Additive White Gaussian Noise (AWGN) model used to characterize the noise of the communication system is no longer suitable for the PLC channel noise. The power line channel noise is divided into background noise and Impulse Noise (IN) according to the characteristics of the noise, the background noise is usually generated by common household appliances and radio broadcasting IN a plurality of frequency bands, and the IN is mainly generated by a series of emergencies such as sudden switching-IN or switching-out of electric equipment. In contrast, the background noise has a small average power and a wide frequency spectrum, similar to AWGN; the IN has strong time-varying property and large power, and has larger influence on signal transmission. The presence of IN degrades the performance of conventional channel estimation techniques, and therefore, accurate channel estimation for MIMO-PLC systems with IN is critical.
For channel estimation techniques, there may be a classification into non-blind channel estimation, blind channel estimation and semi-blind channel estimation. (1) The basic principle of non-blind channel estimation is to insert some known signals, i.e. pilots, at appropriate positions of data to be transmitted, after receiving signals at a receiving end, the signals at the positions of the pilots are first extracted, and CSI at the positions can be estimated by a channel estimation algorithm according to the signals. Among pilot-based channel estimation algorithms, two channel estimation algorithms that are the widest in application range are: least Square (LS) algorithm, Minimum Mean Square Error (MMSE) algorithm, and many others have been developed from these two classical channel estimation algorithms. The non-blind channel estimation can also utilize a Compressed Sensing (CS) theory to convert the channel estimation into a reconstruction problem of a sparse signal (that is, the number of non-zero elements is much smaller than the total number of elements) by utilizing the channel sparse characteristics, and the number of pilots can be effectively reduced. (2) The blind channel estimation algorithm does not need to insert pilot frequency in the useful data to be transmitted, so that the blind channel estimation can obtain high frequency band utilization rate, but correspondingly, the algorithm complexity of the channel estimation is greatly increased, and finally, the communication rate of the whole system is reduced. (3) The semi-blind channel estimation algorithm integrates the above two algorithms. Compared with a blind channel estimation algorithm, the algorithm has a faster convergence speed. The basic principle is as follows: a small number of pilot frequencies are inserted into the useful data to be transmitted, and the statistical characteristics of the pilot frequencies and the transmitted signals are utilized to carry out channel estimation at the receiving end. However, this type of algorithm is more suitable for time invariant channels, and has poor performance in PLC channels. In combination with the above comparison, the research of the channel estimation method in the PLC system mainly focuses on the LS algorithm, MMSE algorithm, CS algorithm and their improved algorithms, but the conventional channel estimation method based on the LS algorithm and MMSE algorithm does not fully utilize the sparse characteristics of the PLC channel, and requires a large amount of pilot information, resulting in large pilot overhead and low spectrum utilization.
For IN suppression techniques, research is currently focused on time-domain nonlinear impulse interference cancellation methods, i.e., "clipping," "zeroing," and combinations thereof, to suppress IN noise by setting a threshold or adaptive threshold. IN the MIMO-PLC system, IN the prior art, channel estimation and IN are mostly considered separately, and an IN suppression module is added at a signal receiving end, so that channel estimation is performed after the influence of IN is reduced. However, accurate channel estimation for MIMO-PLC systems with IN is crucial, so considering IN suppression or channel estimation alone does not achieve ideal performance IN practical applications.
In summary, in the prior art, when estimating a channel, channel estimation and impulse noise are considered separately, and there is a technical problem that the accuracy of a channel estimation result is poor.
Disclosure of Invention
The invention provides a method, a system and equipment for jointly estimating channel impulse response and impulse noise, which are used for solving the technical problem that the accuracy of a channel estimation result is poor due to the fact that channel estimation and impulse noise are considered separately when a channel is estimated in the prior art.
The invention provides a channel impulse response and impulse noise joint estimation method, which comprises the following steps:
s1: establishing an MIMO-PLC channel model, and calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model;
s2: establishing a noise model based on background noise and impulse noise, and calculating the noise of each receiving end of the MIMO-PLC channel according to the established noise model;
s3: calculating to obtain MIMO-PLC channel receiving signals according to the channel frequency response of each receiving end of the MIMO-PLC channel and the noise of each receiving end of the MIMO-PLC channel;
s4: converting the MIMO-PLC channel received signals into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel impulse response matrix;
s5: marking a pilot frequency inserting position in an MIMO-PLC channel receiving signal matrix, and constructing a measuring matrix, an observation matrix and a sparse target signal on the basis of marking the MIMO-PLC channel receiving signal matrix of the pilot frequency inserting position according to the correlation of a channel, the sparse characteristic of channel impact response and the sparse characteristic of impulse noise;
s6: and based on the measurement matrix, the observation matrix and the sparse target signal, performing joint estimation on the impulse noise of the channel and the channel impulse response by adopting a fast block sparse Bayesian learning method to obtain an impulse noise estimation value of the channel and an estimation value of the channel impulse response.
Preferably, the established MIMO-PLC channel model is a 2 x 2MIMO-PLC channel model.
Preferably, the specific process of calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model is as follows;
calculating the channel frequency response of a single-input single-output channel in the MIMO-PLC channel model;
and combining the channel frequency responses of the single input and single output channels to obtain the channel frequency response of each receiving end of the MIMO-PLC channel model.
Preferably, in the noise model, white gaussian noise is used to describe the background noise, and bernoulli-gaussian model is used to describe the impulse noise.
Preferably, the specific process of step S4 is:
converting the MIMO-PLC channel received signals into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel frequency response matrix;
and replacing a channel frequency response matrix in the MIMO-PLC channel received signal matrix with a channel impulse response matrix to obtain the MIMO-PLC channel received signal matrix containing the channel impulse response matrix.
Preferably, the specific process of step S6 is:
s601: initializing the measurement matrix YBObservation matrix
Figure GDA0003542618170000041
And sparse target signal Z, wherein the OFDM symbol length is N, the total number of blocks is g, and the length of each sub-block is diLet the correlation vector
Figure GDA0003542618170000042
All the elements of (A) are zero;
s602: if the signal-to-noise ratio of the MIMO-PLC channel receiving signal is less than 20dB, making beta-1＝0.1||YB||2If the signal-to-noise ratio of the MIMO-PLC channel receiving signal is more than 20dB, the signal-to-noise ratio is enabled to be beta-1＝0.01||YB||2(ii) a Order to
Figure GDA0003542618170000043
Wherein i ∈ [1, g ]]；
S603: computing block covariance matrices
Figure GDA0003542618170000044
Block dependencies
Figure GDA0003542618170000045
Correlation structure matrix Bi＝Ai/γi；
S604: reconstruction from related structural constraints
Figure GDA0003542618170000046
Calculating a cost function difference using a cost function L (i)
Figure GDA0003542618170000047
S605: order to
Figure GDA0003542618170000048
Updating parameters mu, sigma, siAnd q isiWhere μ, Σ is the posterior probability density p (Z | Y) of the sparse target signal ZB,{γi,Bi}, β);
s606: according to the updated siAnd q isiRecalculating block covariance matrices
Figure GDA0003542618170000049
Block dependencies
Figure GDA00035426181700000410
Correlation structure matrix Bi＝Ai/γiThe updated correlation vector γ is denoted as γnew；
S607: judgment of
Figure GDA00035426181700000411
Whether the judgment is true or not, wherein eta is a preset threshold value; if yes, obtaining the desired sparse objectThe standard signal Z is mu, wherein the first 4N row elements of Z are the estimated values of the channel impulse response, and the last 4N row elements are the estimated values of the impulse noise of the channel; if not, the steps S602-S605 are executed again.
Preferably, in step S607, η is equal to 10-4。
Preferably, in step S604, the formula of the cost function l (i) is:
Figure GDA0003542618170000051
wherein I is an identity matrix.
A channel impulse response and impulse noise joint estimation system comprises a channel frequency response calculation module, a noise calculation module, a channel receiving signal conversion module, a matrix construction module and a joint estimation module;
the channel frequency response calculation module is used for establishing an MIMO-PLC channel model and calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model;
the noise calculation module is used for establishing a noise model based on background noise and impulse noise and calculating the noise of each receiving end of the MIMO-PLC channel according to the established noise model;
the channel receiving signal calculating module is used for calculating to obtain MIMO-PLC channel receiving signals according to the channel frequency response of each receiving end of the MIMO-PLC channel and the noise of each receiving end of the MIMO-PLC channel;
the channel receiving signal conversion module is used for converting the MIMO-PLC channel receiving signals into a matrix form to obtain an MIMO-PLC channel receiving signal matrix containing a channel impact response matrix;
the matrix construction module is used for marking the position of pilot frequency insertion in the MIMO-PLC channel receiving signal matrix, and constructing a measurement matrix, an observation matrix and a sparse target signal on the basis of the MIMO-PLC channel receiving signal matrix marked with the pilot frequency insertion position according to the correlation of the channel, the sparse characteristic of channel impact response and the sparse characteristic of impulse noise;
the joint estimation module is used for carrying out joint estimation on the impulse noise and the channel impulse response of the channel by adopting a fast block sparse Bayesian learning method based on the measurement matrix, the observation matrix and the sparse target signal to obtain an impulse noise estimation value and an impulse response estimation value of the channel.
A channel impulse response and impulse noise joint estimation device comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform a channel impulse response and impulse noise joint estimation method as described above according to instructions in the program code.
According to the technical scheme, the embodiment of the invention has the following advantages:
according to the embodiment of the invention, the channel frequency response and the noise of each receiving end of the MIMO-PLC channel are respectively solved by establishing an MIMO-PLC channel model and a noise model, and the MIMO-PLC channel receiving signal is calculated on the basis; by utilizing the correlation of a channel and the sparse characteristics of channel impact response and impulse noise, the structure and space distribution information of a physical signal can be effectively mined by utilizing block sparsity based on a fast block sparse Bayesian learning (BSBL-FM) method, so that the performance of a sparse reconstruction algorithm is remarkably improved.
The embodiment provided by the invention also has the following other characteristics:
the embodiment of the invention simplifies the structure of the receiver, does not need to design an impulse noise suppression algorithm before channel estimation, and can greatly reduce the influence of impulse noise on communication quality by simply subtracting the estimated value of the impulse noise at a receiving end; compared with the traditional LS algorithm and MMSE algorithm, the embodiment of the invention can still keep good performance under the condition of reducing the number of the pilot frequency, and improves the transmission efficiency and the reliability of the MIMO-PLC system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art 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 for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a method, a system and a device for jointly estimating channel impulse response and impulse noise according to an embodiment of the present invention.
Fig. 2 is a block diagram of a MIMO-PLC system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a 2 × 2MIMO-PLC channel model according to an embodiment of the present invention.
Fig. 4 is a system framework diagram of a method, a system, and a device for jointly estimating impulse response and impulse noise according to an embodiment of the present invention.
Fig. 5 is a device framework diagram of a method, a system, and a device for jointly estimating channel impulse response and impulse noise according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a system and equipment for jointly estimating channel impulse response and impulse noise, which are used for solving the technical problem that the accuracy of a channel estimation result is poor due to the fact that channel estimation and impulse noise are considered separately when a channel is estimated in the prior art.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method, a system and a device for jointly estimating channel impulse response and impulse noise according to an embodiment of the present invention.
Fig. 2 is a block diagram of the MIMO-PLC system in the present embodiment. The specific process is as follows: an input data stream (including a data signal and a pilot signal) is mapped into an OFDM symbol after modulation and coding, and the OFDM symbol is sent into an MIMO-PLC channel after serial-parallel conversion, Inverse Fast Fourier Transform (IFFT) operation, Cyclic Prefix (CP) insertion and parallel-serial conversion. After serial-to-parallel conversion, CP removal, Fast Fourier Transform (FFT) operation and parallel-to-serial conversion are carried out on signals received by a receiving end, signals at pilot frequency positions are extracted, joint estimation of channel impulse response (CSI) and Impulse Noise (IN) is carried out, estimated values are obtained and then used for IN elimination and channel equalization, and data are output after demodulation.
As shown in fig. 1, a method, a system and a device for jointly estimating channel impulse response and impulse noise according to an embodiment of the present invention include the following steps:
s1: in household cables, there are three transmission lines: phase line (P), neutral line (N), protective earth line (PE). The single input single output (SISO-PLC) system only uses P-N to transmit information, and the MIMO-PLC system can use a plurality of pairs of transmission lines (namely P-N, P-PE and PE-N) to transmit data, thereby effectively improving the system capacity; therefore, an MIMO-PLC channel model is established according to a plurality of pairs of transmission lines, and the channel frequency response of each receiving end of the MIMO-PLC channel is calculated on the basis of the established MIMO-PLC channel model;
s2: various types of noise and interference conditions in the PLC are very complex, and the PLC is generally divided into two types: background noise and Impulse Noise (IN), wherein the background noise comprises colored background noise, narrow-band noise and power frequency asynchronous periodic impulse noise; the Impulse Noise (IN) includes power frequency synchronous periodic impulse noise and asynchronous impulse noise. The average power of background noise is small, the frequency spectrum is very wide and is similar to white noise; the Impulse Noise (IN) has strong time-varying property and large power, and has larger influence on signal transmission. Based on the method, a noise model is established according to background noise and impulse noise, and the noise and the probability density of the noise of each receiving end of the MIMO-PLC channel are calculated according to the established noise model;
s3: after solving the channel frequency response of each receiving end of the MIMO-PLC channel and the noise of each receiving end, calculating to obtain the MIMO-PLC channel receiving signal of each receiving end, specifically, representing the MIMO-PLC channel receiving signal by combining the channel frequency response of each receiving end and the noise meter of each receiving end to obtain a formula of the MIMO-PLC channel receiving signal, and calculating the MIMO-PLC channel receiving signal according to the formula;
s4: in order to facilitate subsequent calculation, converting the MIMO-PLC channel received signals into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel frequency response matrix; replacing a channel frequency response matrix in the MIMO-PLC channel received signal matrix with a channel impulse response matrix to obtain an MIMO-PLC channel received signal matrix containing the channel impulse response matrix;
s5: the position of pilot frequency insertion is marked in a receiving signal matrix of an MIMO-PLC channel, and because a transmission channel of the PLC is a multi-path model, namely, a signal reaches a receiving end through a plurality of paths when being transmitted on the PLC channel, and the signal energy is gradually reduced along with the continuous reflection of the signal on a power line, the energy of the transmission signal is mainly concentrated on the first paths with smaller time delay, namely, the channel impact of the PLC channel correspondingly has the sparse characteristic, and the impulse noise of a time domain also has the sparse characteristic; therefore, according to the correlation of the channel, the sparse characteristic of channel impulse response and the sparse characteristic of impulse noise, on the basis of the MIMO-PLC channel received signal matrix marking the position where the pilot frequency is inserted, a measurement matrix, an observation matrix and a sparse target signal are constructed, and the joint estimation problem is converted into a typical compressed sensing problem;
s6: and based on the measurement matrix, the observation matrix and the sparse target signal, performing joint estimation on the impulse noise of the channel and the channel impulse response by adopting a fast block sparse Bayesian learning method to obtain an impulse noise estimation value of the channel and an estimation value of the channel impulse response. It should be further noted that the block sparsity is a typical structured sparse form in nature and information space, and compared with a point sparse model, the block sparse representation can effectively mine the structural and spatial distribution information of a physical signal, thereby significantly improving the performance of a sparse reconstruction algorithm. The sparse Bayesian learning method can fully mine and utilize prior information of data, supposing the probability distribution condition of the prior information, reasonably and mathematically model the problem to be solved, so as to realize the learning of a low-dimensional model, and realize the optimal sparse representation of signals and images by utilizing the characteristics of the data.
Example 2
As shown in fig. 1, a method, a system and a device for jointly estimating channel impulse response and impulse noise according to an embodiment of the present invention include the following steps:
s1: in household cables, there are three transmission lines: phase line (P), neutral line (N), protective earth line (PE). The single input single output (SISO-PLC) system only uses P-N to transmit information, and the MIMO-PLC system can use a plurality of pairs of transmission lines (namely P-N, P-PE and PE-N) to transmit data, thereby effectively improving the system capacity; therefore, an MIMO-PLC channel model is established according to a plurality of pairs of transmission lines, and the channel frequency response of each receiving end of the MIMO-PLC channel is calculated on the basis of the established MIMO-PLC channel model;
it should be further noted that, the embodiment constructs a 2 × 2MIMO-PLC channel model, the specific structure of which is shown in fig. 3,
by H(mn)(f) Indicating the Channel Frequency Response (CFR) from the mth transmitting end to the nth receiving end. A top-down statistical model of multipath channels for SISO-PLC systems, i.e. H(11)(f) Represented by the following formula:
Figure GDA0003542618170000091
wherein N ispIs the total number of paths, gpGain for the p-th path, dpIs the length of the p-th path, v is the propagation velocity of the electromagnetic wave, a0、a1And k is an attenuation parameter.
For the MIMO-PLC channel, considering symmetry of the power line network and spatial correlation exhibited by the sub-channels, the MIMO-PLC channel is decomposed into a combination of multiple SISO-PLC channels, so the channel frequency response of each receiving end is expressed as:
Figure GDA0003542618170000092
wherein the content of the first and second substances,
Figure GDA0003542618170000093
the channel spatial correlation is represented for each path's random phase.
S2: various types of noise and interference conditions in the PLC are very complex, and the PLC is generally divided into two types: background noise and Impulse Noise (IN), wherein the background noise comprises colored background noise, narrow-band noise and power frequency asynchronous periodic impulse noise; the Impulse Noise (IN) includes power frequency synchronous periodic impulse noise and asynchronous impulse noise. The average power of background noise is small, the frequency spectrum is very wide and is similar to white noise; the Impulse Noise (IN) has strong time-varying property and large power, and has larger influence on signal transmission. Based on the above, a noise model is established according to the background noise and the impulse noise, and the noise and the probability density of the noise of each receiving end of the MIMO-PLC channel are calculated according to the established noise model, which specifically comprises the following steps:
noise of nth receiving end of MIMO-PLC channel(n)Expressed as:
noise(n)＝g(n)+i(n) (3)
wherein, g(n)Is a mean of 0 and a variance of σg(ii) a gaussian random process; i.e. i(n)Is the product of the bernoulli random process and the gaussian random process, i.e.:
Figure GDA0003542618170000101
where P is the probability of occurrence of IN, r(n)Is a mean of 0 and a variance of σrAnd g with the Gaussian random process of(n)Independently of one another, i.e. equation (3) is re-expressed as:
Figure GDA0003542618170000102
thus, its probability density is expressed as:
Figure GDA0003542618170000103
s3: after solving the channel frequency response of each receiving end of the MIMO-PLC channel and the noise of each receiving end, the MIMO-PLC channel received signal of each receiving end can be calculated, specifically, the MIMO-PLC channel received signal is characterized by combining the channel frequency response of each receiving end and the noise meter of each receiving end to obtain the formula of the MIMO-PLC channel received signal, and the MIMO-PLC channel received signal can be calculated according to the formula, and the specific process is as follows:
the OFDM frequency domain signal vector transmitted by the mth transmitting port in the MIMO-PLC system is defined as X(m)＝[X1,X2，...XN]TThe length of the OFDM symbol is N;
the signal Y received by the nth receiving port(mn)Expressed as:
Figure GDA0003542618170000104
wherein H(mn)＝[H1,H2，...HN]TIs a CFR vector; fNRepresenting an N x N dimensional discrete Fourier transform matrix; g(n)Representing the noise g against the background(n)Performing a discrete Fourier transform, G(n)Still AWGN;
Figure GDA0003542618170000105
representing the hadamard product.
S4: in order to facilitate subsequent calculation, converting the MIMO-PLC channel received signals into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel frequency response matrix; the method comprises the following steps of replacing a channel frequency response matrix in an MIMO-PLC channel received signal matrix with a channel impulse response matrix to obtain the MIMO-PLC channel received signal matrix containing the channel impulse response matrix, wherein the specific process comprises the following steps:
rewriting formula (7) as:
Y(mn)＝diag(X(m))H(mn)+FNi(n)+G(n) (8)
wherein, diag (X)(m)) The expression element being a vector X(m)A diagonal matrix of medium elements.
Extending to the whole MIMO-PLC channel, the MIMO-PLC channel receiving signal is expressed as:
Figure GDA0003542618170000111
the expression (9) is abbreviated as matrix:
Y＝XH+Fi+G (10)
replacing the CFR matrix (channel frequency response matrix) in equation (10) with the CIR matrix (channel impulse response matrix), equation (10) being expressed as:
Y＝XFh+Fi+G (11)
s5: the position of pilot frequency insertion is marked in a receiving signal matrix of the MIMO-PLC channel, and because a transmission channel of the PLC is a multi-path model, namely, a signal reaches a receiving end through a plurality of paths when being transmitted on the PLC channel, and the signal energy is gradually reduced along with the continuous reflection of the signal on a power line, the energy of the transmission signal is mainly concentrated on the first paths with smaller time delay, namely, the channel impact of the PLC channel correspondingly has the sparse characteristic, and the impulse noise of a time domain also has the sparse characteristic; therefore, according to the correlation of the channel, the sparse characteristic of channel impulse response and the sparse characteristic of impulse noise, on the basis of the MIMO-PLC channel received signal matrix marking the position where the pilot frequency is inserted, a measurement matrix, an observation matrix and a sparse target signal are constructed, and the joint estimation problem is converted into a typical compressed sensing problem;
defining the set of positions where pilots are inserted in the transmitted signal as B, (-)BFor the submatrix formed by the index corresponding rows or elements in the set B, equation (11) is transformed into:
YB＝XBFBh+FBi+GB (12)
according to the correlation of the channel, the sparse characteristic of the channel impulse response and the sparse characteristic of the impulse noise, the formula (12) is transformed into the following formula:
Figure GDA0003542618170000121
let phi become [ X ]BFB FB]，Z＝[hT iT]TThe joint estimation problem transforms into a typical compressed sensing problem:
YB＝ΦZ+GB (14)
wherein, YBFor the measurement matrix, phi is the observation matrix, Z is the sparse target signal, GBIs AWGN.
S6: and based on the measurement matrix, the observation matrix and the sparse target signal, performing joint estimation on the impulse noise of the channel and the channel impulse response by adopting a fast block sparse Bayesian learning method to obtain an impulse noise estimation value of the channel and an estimation value of the channel impulse response. It should be further noted that the block sparsity is a typical structured sparse form in nature and information space, and compared with a point sparse model, the block sparse representation can effectively mine the structural and spatial distribution information of a physical signal, thereby significantly improving the performance of a sparse reconstruction algorithm. The sparse Bayesian learning method can fully mine and utilize prior information of data, reasonably and mathematically model the problem to be solved by assuming the probability distribution condition of the prior information to realize the learning of a low-dimensional model, and realize the optimal sparse representation of signals and images by utilizing the characteristics of the data; the specific process is as follows:
s601: initializing the measurement matrix YBAnd [ phi ] is observed as matrix phi1，...,Φg]And sparse target signal Z, wherein the OFDM symbol length is N, the total number of blocks is g, and the length of each sub-block is diLet the correlation vector
Figure GDA0003542618170000122
All the elements of (A) are zero;
s602: if the signal-to-noise ratio of the MIMO-PLC channel receiving signal is less than 20dB, making beta-1＝0.1||YB||2If the signal-to-noise ratio of the MIMO-PLC channel receiving signal is more than 20dB, the signal-to-noise ratio is enabled to be beta-1＝0.01||YB||2(ii) a Order to
Figure GDA0003542618170000123
Wherein i ∈ [1, g ]]；
S603: computing block covariance matrices
Figure GDA0003542618170000124
Block dependencies
Figure GDA0003542618170000125
Correlation structure matrix Bi＝Ai/γi；
S604: reconstruction from related structural constraints
Figure GDA0003542618170000126
The specific process is as follows:
to exploit the correlation of the sparse target signal Z, pair BiAdding constraints, where i ∈ [1, g ]]Provision of BiIn the Toeplitz form, i.e.
Figure GDA0003542618170000131
Wherein the correlation coefficient riThe definition is as follows:
Figure GDA0003542618170000132
wherein the content of the first and second substances,
Figure GDA0003542618170000133
is BiThe mean of the secondary diagonal elements,
Figure GDA0003542618170000134
is the mean of the diagonal elements; to ensure the positive nature of the reconstructed Toeplitz matrix, a constraint | r needs to be addediI < 0.99, i.e.
Figure GDA0003542618170000135
All partitioned sub-blocks within the signal Z often conform to a similar correlation structure. In this case, all signal sub-blocks Z in the sparse signal Z are assumediAll have the same correlation coefficient r:
Figure GDA0003542618170000136
the correlation structure matrix BiCan be obtained by r reconstruction
Figure GDA0003542618170000137
According to formula (19) of
Figure GDA0003542618170000138
To obtain
Figure GDA0003542618170000139
Calculating a cost function difference using a cost function L (i)
Figure GDA00035426181700001310
The cost function L (i) has the formula:
Figure GDA00035426181700001311
wherein I is an identity matrix.
S605: order to
Figure GDA00035426181700001312
Updating parameters mu, sigma, siAnd q isiWhere μ, Σ is the posterior probability density p (Z | Y) of the sparse target signal ZB,{γi,Bi}, β); as follows:
and (3) updating and setting parameters:
at algorithm initialization, all signal sub-blocks Z are assumedi，
Figure GDA00035426181700001313
Are not added into gamma, each signal sub-block ZiConsidering a base, at the kth iteration, the set of indices that have been added to the base in γ is defined as Ik(e.g., assuming that the signal is divided into 32 blocks of g, IkIs Ik＝{1,7,9})。
Redefines Φ as a new matrix of existing bases in the current γ, and in the (k +1) th iteration, we denote the subscript of the selected updated base with i (where i ∈ {1, 2.. g }). For all bases, it is uniformly numbered with m (where m ∈ {1, 2.., g }). For the parameter to be updated in the (k +1) th step, the sign is used
Figure GDA00035426181700001411
And (4) showing.
Wherein, for the convenience of calculation, define
Figure GDA0003542618170000141
Figure GDA0003542618170000142
Then si，qiAnd Si，QiHas a corresponding relationship of
si＝(I-SiAi)-1Si
qi＝(I-SiAi)-1Qi
For the ith base, at (k +1) iterations, if
Figure GDA0003542618170000143
Then there is AiWhen the value is 0, then:
si＝Si
qi＝Qi
and (3) parameter updating operation:
in obtaining all
Figure GDA0003542618170000144
Figure GDA0003542618170000145
Then according to
Figure GDA0003542618170000146
Selecting the ith radical for operation, in particular
Figure GDA0003542618170000147
The updating mode comprises the following two modes:
(1) add operation parameter updating mode
Figure GDA0003542618170000148
Figure GDA0003542618170000149
Definition ei＝β(Φi-βΦΣΦTΦi)
Figure GDA00035426181700001410
To Si，QiUpdating:
Figure GDA0003542618170000151
Figure GDA0003542618170000152
according to si，qiAnd Si，QiCan be used for si，qiAnd (6) updating.
(2) Re-estimate operation parameter updating mode
Definition of
Figure GDA0003542618170000153
Then there is:
Figure GDA0003542618170000154
Figure GDA0003542618170000155
Figure GDA0003542618170000156
to Si，QiIs updated to obtain
Figure GDA0003542618170000157
Figure GDA0003542618170000158
According to si，qiAnd Si，QiCan be used for si，qiAnd (6) updating.
Mu, sigma is the posterior probability density p (Z | Y) of the sparse target signal ZB,{γi,Bi}, β) as follows:
Figure GDA0003542618170000159
wherein the content of the first and second substances,
Σ-1＝Γ-1+ΦTβΦ
μ＝ΣΦTβYB
Γ＝diag-1(γ1B1,...,γgBg)＝diag-1(A1,...,Ag)
likelihood function p (Y)B|{γi,Bi}, beta) is as follows
Figure GDA00035426181700001510
Wherein C is beta-1I+ΦΓΦTDefinition of
Figure GDA00035426181700001511
Then siAnd q isiSatisfy at update
Figure GDA00035426181700001512
S606: according to the updated siAnd q isiRecalculating block covariance matrices
Figure GDA0003542618170000161
Block dependencies
Figure GDA0003542618170000162
Correlation structure matrixBi＝Ai/γiThe updated correlation vector γ is denoted as γnew；
S607: judgment of
Figure GDA0003542618170000163
If yes, wherein eta is a preset threshold value, and eta is 10-4If yes, the obtained sparse target signal Z is obtained as mu, wherein the first 4N row elements of Z are estimated values of channel impact response, namely
Figure GDA0003542618170000164
The last 4N row elements are estimated values of the impulse noise of the channel; namely, it is
Figure GDA0003542618170000165
If not, the steps S602-S605 are executed again.
After impulse response and impulse noise of the MIMO-PLC channel are jointly estimated, an estimated value of the impulse noise is obtained
Figure GDA0003542618170000166
The noise cancellation process is represented as:
Figure GDA0003542618170000167
after impulse noise suppression, the remaining IN can be considered as background noise.
It should be further noted that, the present embodiment provides a method for jointly estimating impulse response and impulse noise in a 2 × 2MIMO-PLC system, and the present embodiment can be extended to other MIMO-PLC systems by changing the matrix dimension in equation (9).
Example 3
As shown in fig. 4, a channel impulse response and impulse noise joint estimation system includes a channel frequency response calculation module 21, a noise calculation module 22, a channel received signal calculation module 23, a channel received signal conversion module 24, a matrix construction module 25, and a joint estimation module 26;
the channel frequency response calculation module 21 is configured to establish an MIMO-PLC channel model, and calculate a channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model;
the noise calculation module 22 is configured to establish a noise model based on the background noise and the impulse noise, and calculate the noise of each receiving end of the MIMO-PLC channel according to the established noise model;
the channel received signal calculating module 23 is configured to calculate a MIMO-PLC channel received signal according to a channel frequency response of each receiving end of the MIMO-PLC channel and noise of each receiving end of the MIMO-PLC channel;
the channel received signal conversion module 24 is configured to convert the MIMO-PLC channel received signals into a matrix form, so as to obtain a MIMO-PLC channel received signal matrix including a channel impulse response matrix;
the matrix construction module 25 is configured to mark a pilot frequency insertion position in the MIMO-PLC channel received signal matrix, and construct a measurement matrix, an observation matrix, and a sparse target signal on the basis of the MIMO-PLC channel received signal matrix marked with the pilot frequency insertion position according to the correlation of the channel, the channel impulse response, and the sparse characteristic of impulse noise;
the joint estimation module 26 is configured to perform joint estimation on the impulse noise and the channel impulse response of the channel by using a fast block sparse bayesian learning method based on the measurement matrix, the observation matrix, and the sparse target signal, so as to obtain an impulse noise estimation value and a channel impulse response estimation value of the channel.
As shown in fig. 5, an electric equipment load data monitoring and analyzing device 3 includes a processor 30 and a memory 31;
the memory 31 is used for storing program codes 32 and transmitting the program codes 32 to the processor;
the processor 30 is configured to execute the steps of the above-mentioned method for monitoring and analyzing load data of electric equipment according to the instructions in the program code 32.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 30.
The terminal device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 3, a memory 31. It will be appreciated by those skilled in the art that fig. 5 is merely an example of the terminal device 3 and does not constitute a limitation of the terminal device 3 and may comprise more or less components than those shown, or some components may be combined, or different components, for example the terminal device may further comprise input output devices, network access devices, buses, etc.
The Processor 300 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf ProgrammaBle Gate Array (FPGA) or other ProgrammaBle logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 301 may be an internal storage unit of the terminal device 30, such as a hard disk or a memory of the terminal device 30. The memory 301 may also be an external storage device of the terminal device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 30. Further, the memory 301 may also include both an internal storage unit and an external storage device of the terminal device 30. The memory 301 is used for storing the computer program and other programs and data required by the terminal device. The memory 301 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (9)

1. A channel impulse response and impulse noise joint estimation method is characterized by comprising the following steps:
s1: establishing an MIMO-PLC channel model, and calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model;
s2: establishing a noise model based on background noise and impulse noise, and calculating the noise of each receiving end of the MIMO-PLC channel according to the established noise model;
s3: the method for obtaining the MIMO-PLC channel receiving signal according to the channel frequency response of each receiving end of the MIMO-PLC channel and the noise calculation of each receiving end of the MIMO-PLC channel specifically comprises the following steps:
the OFDM frequency domain signal vector transmitted by the mth transmitting port in the MIMO-PLC system is defined as X(m)＝[X1,X2，...XN]TThe length of the OFDM symbol is N;
the signal Y received by the nth receiving port(mn)Expressed as:
Figure FDA0003542618160000011
wherein H(mn)＝[H1,H2，...HN]TIs a CFR vector; fNRepresenting an N x N dimensional discrete Fourier transform matrix; g(n)Representing the noise g against the background(n)Performing a discrete Fourier transform, G(n)Still AWGN;
Figure FDA0003542618160000013
representing a Hadamard product;
s4: converting the MIMO-PLC channel received signal into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel impulse response matrix, and specifically comprising:
rewriting formula (7) as:
Y(mn)＝diag(X(m))H(mn)+FNi(n)+G(n) (8)
wherein, diag (X)(m)) The expression element being a vector X(m)Diagonal matrix of middle elements, i(n)Is the product of a Bernoulli random process and a Gaussian random process;
extending to the whole MIMO-PLC channel, the MIMO-PLC channel receiving signal is expressed as:
Figure FDA0003542618160000012
the expression (9) is abbreviated as matrix:
Y＝XH+Fi+G (10)
replacing the CFR matrix (channel frequency response matrix) in equation (10) with the CIR matrix (channel impulse response matrix), equation (10) being expressed as:
Y＝XFh+Fi+G (11)
wherein, F represents an NxN dimensional discrete Fourier transform matrix, and h is a CIR vector;
s5: marking the position of pilot frequency insertion in the MIMO-PLC channel receiving signal matrix, and constructing a measurement matrix, an observation matrix and a sparse target signal on the basis of marking the MIMO-PLC channel receiving signal matrix of the pilot frequency insertion position according to the correlation of a channel, the sparse characteristic of channel impulse response and the sparse characteristic of impulse noise, wherein the method specifically comprises the following steps:
defining the set of positions where pilots are inserted in the transmitted signal as B, (-)BFor the submatrix formed by the index corresponding rows or elements in the set B, equation (11) is transformed into:
YB＝XBFBh+FBi+GB (12)
according to the correlation of the channel, the sparse characteristic of the channel impulse response and the sparse characteristic of the impulse noise, the formula (12) is transformed into the following formula:
Figure FDA0003542618160000021
let phi become [ X ]BFB FB]，Z＝[hT iT]TThe joint estimation problem transforms into a typical compressed sensing problem:
YB＝ΦZ+GB (14)
wherein, YBFor the measurement matrix, phi is the observation matrix, Z is the sparse target signal, GBIs AWGN;
s6: based on the measurement matrix, the observation matrix and the sparse target signal, performing joint estimation on the impulse noise of the channel and the channel impulse response by using a fast block sparse bayesian learning method to obtain an impulse noise estimation value of the channel and an estimation value of the channel impulse response, wherein the S6 specifically comprises the following steps:
s601: initializing the measurement matrix YBObservation matrix
Figure FDA0003542618160000022
And sparse target signal Z, wherein the OFDM symbol length is N, the total number of blocks is g, and the length of each sub-block is diLet the correlation vector
Figure FDA0003542618160000023
All the elements of (A) are zero;
s602: if the signal-to-noise ratio of the MIMO-PLC channel receiving signal is less than 20dB, making beta-1＝0.1||YB||2If the signal-to-noise ratio of the MIMO-PLC channel receiving signal is more than 20dB, the signal-to-noise ratio is enabled to be beta-1＝0.01||YB||2(ii) a Order to
Figure FDA0003542618160000024
Wherein i is [1, g ]]The whole number of (1);
s603: computing block covariance matrices
Figure FDA0003542618160000025
Block dependencies
Figure FDA0003542618160000031
Correlation structure matrix Bi＝Ai/γi；
S604: reconstruction from related structural constraints
Figure FDA0003542618160000032
Calculating a cost function difference using a cost function L (i)
Figure FDA0003542618160000033
S605: order to
Figure FDA0003542618160000034
Updating parameters mu, sigma, siAnd q isiWhere μ, Σ is the posterior probability density p (Z | Y) of the sparse target signal ZB,{γi,Bi}, β);
s606: according to the updated siAnd q isiRecalculating block covariance matrices
Figure FDA0003542618160000035
Block dependencies
Figure FDA0003542618160000036
Correlation structure matrix Bi＝Ai/γiThe updated correlation vector γ is denoted as γnew；
S607: judgment of
Figure FDA0003542618160000037
Whether the judgment is true or not, wherein eta is a preset threshold value; if yes, obtaining the obtained sparse target signal Z as mu, wherein the first 4N row elements of Z are estimated values of channel impulse response, and the last 4N row elements are estimated values of impulse noise of a channel; if not, the steps S602-S605 are executed again.
2. The method of claim 1, wherein the established MIMO-PLC channel model is a 2 x 2MIMO-PLC channel model.
3. The method for joint estimation of channel impulse response and impulse noise according to claim 1, wherein the specific process of calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model is;
calculating the channel frequency response of a single-input single-output channel in the MIMO-PLC channel model;
and combining the channel frequency responses of the single input and single output channels to obtain the channel frequency response of each receiving end of the MIMO-PLC channel model.
4. The method as claimed in claim 1, wherein in the noise model, white gaussian noise is used to describe the background noise, and bernoulli-gaussian noise is used to describe the impulse noise.
5. The method of claim 1, wherein the specific process of step S4 is as follows:
converting the MIMO-PLC channel received signals into a matrix form to obtain an MIMO-PLC channel received signal matrix containing a channel frequency response matrix;
and replacing a channel frequency response matrix in the MIMO-PLC channel received signal matrix with a channel impulse response matrix to obtain the MIMO-PLC channel received signal matrix containing the channel impulse response matrix.
6. The method as claimed in claim 1, wherein in step S607, η is 10 ═ 10-4。
7. The method of claim 6, wherein in step S604, the cost function l (i) is expressed by the following formula:
Figure FDA0003542618160000041
wherein I is an identity matrix.
8. A channel impulse response and impulse noise joint estimation system is characterized by comprising a channel frequency response calculation module, a noise calculation module, a channel receiving signal conversion module, a matrix construction module and a joint estimation module;
the channel frequency response calculation module is used for establishing an MIMO-PLC channel model and calculating the channel frequency response of each receiving end of the MIMO-PLC channel according to the MIMO-PLC channel model;
the noise calculation module is used for establishing a noise model based on background noise and impulse noise and calculating the noise of each receiving end of the MIMO-PLC channel according to the established noise model;
the channel received signal calculation module is configured to calculate, according to the channel frequency response of each receiving end of the MIMO-PLC channel and the noise of each receiving end of the MIMO-PLC channel, a MIMO-PLC channel received signal, and specifically includes:
the OFDM frequency domain signal vector transmitted by the mth transmitting port in the MIMO-PLC system is defined as X(m)＝[X1,X2，...XN]TThe length of the OFDM symbol is N;
the signal Y received by the nth receiving port(mn)Expressed as:
Figure FDA0003542618160000042
wherein H(mn)＝[H1,H2，...HN]TIs a CFR vector; fNRepresenting an N x N dimensional discrete Fourier transform matrix; g(n)Representing the noise g against the background(n)Performing a discrete Fourier transform, G(n)Still AWGN;
Figure FDA0003542618160000043
representing a Hadamard product;
the channel received signal conversion module is used for converting the MIMO-PLC channel received signals into a matrix form to obtain a MIMO-PLC channel received signal matrix including a channel impulse response matrix, and specifically includes:
rewriting formula (7) as:
Y(mn)＝diag(X(m))H(mn)+FNi(n)+G(n) (8)
wherein, diag (X)(m)) The expression element being a vector X(m)Diagonal matrix of middle elements, i(n)Is the product of a Bernoulli random process and a Gaussian random process;
extending to the whole MIMO-PLC channel, the MIMO-PLC channel receiving signal is expressed as:
Figure FDA0003542618160000051
the expression (9) is abbreviated as matrix:
Y＝XH+Fi+G (10)
replacing the CFR matrix (channel frequency response matrix) in equation (10) with the CIR matrix (channel impulse response matrix), equation (10) being expressed as:
Y＝XFh+Fi+G (11)
wherein, F represents an NxN dimensional discrete Fourier transform matrix, and h is a CIR vector;
the matrix construction module is used for marking the position of pilot frequency insertion in the MIMO-PLC channel receiving signal matrix, and constructing a measurement matrix, an observation matrix and a sparse target signal on the basis of marking the MIMO-PLC channel receiving signal matrix of the pilot frequency insertion position according to the correlation of the channel, the sparse characteristic of channel impulse response and the sparse characteristic of impulse noise, and specifically comprises the following steps:
defining the set of positions where pilots are inserted in the transmitted signal as B, (-)BFor the submatrix formed by the index corresponding rows or elements in the set B, equation (11) is transformed into:
YB＝XBFBh+FBi+GB (12)
according to the correlation of the channel, the sparse characteristic of the channel impulse response and the sparse characteristic of the impulse noise, the formula (12) is transformed into the following formula:
Figure FDA0003542618160000052
let phi become [ X ]BFB FB]，Z＝[hT iT]TThe joint estimation problem transforms into a typical compressed sensing problem:
YB＝ΦZ+GB (14)
wherein, YBFor the measurement matrix, phi is the observation matrix, Z is the sparse target signal, GBIs AWGN;
the joint estimation module is used for performing joint estimation on the impulse noise and the channel impulse response of the channel by adopting a fast block sparse Bayesian learning method based on a measurement matrix, an observation matrix and a sparse target signal to obtain an impulse noise estimation value and an estimation value of the channel impulse response of the channel, and the specific process of performing joint estimation on the impulse noise and the channel impulse response of the channel by adopting the fast block sparse Bayesian learning method based on the measurement matrix, the observation matrix and the sparse target signal to obtain the impulse noise estimation value and the estimation value of the channel impulse response of the channel is as follows:
s601: initializing the measurement matrix YBObservation matrix
Figure FDA0003542618160000061
And sparse target signal Z, wherein the OFDM symbol length is N, the total number of blocks is g, and the length of each sub-block is diLet the correlation vector
Figure FDA0003542618160000062
All the elements of (A) are zero;
s602: if the signal-to-noise ratio of the MIMO-PLC channel receiving signal is less than 20dB, making beta-1＝0.1||YB||2If the signal-to-noise ratio of the MIMO-PLC channel receiving signal is more than 20dB, the signal-to-noise ratio is enabled to be beta-1＝0.01||YB||2(ii) a Order to
Figure FDA0003542618160000024
Wherein i is [1, g ]]The whole number of (1);
s603: computing block covariance matrices
Figure FDA0003542618160000064
Block dependencies
Figure FDA0003542618160000065
Correlation structure matrix Bi＝Ai/γi；
S604: reconstruction from related structural constraints
Figure FDA0003542618160000066
By usingCost function L (i) calculating a cost function difference
Figure FDA0003542618160000067
S605: order to
Figure FDA0003542618160000068
Updating parameters mu, sigma, siAnd q isiWhere μ, Σ is the posterior probability density p (Z | Y) of the sparse target signal ZB,{γi,Bi}, β);
s606: according to the updated siAnd q isiRecalculating block covariance matrices
Figure FDA0003542618160000069
Block dependencies
Figure FDA00035426181600000610
Correlation structure matrix Bi＝Ai/γiThe updated correlation vector γ is denoted as γnew；
S607: judgment of
Figure FDA00035426181600000611
Whether the judgment is true or not, wherein eta is a preset threshold value; if yes, obtaining the obtained sparse target signal Z as mu, wherein the first 4N row elements of Z are estimated values of channel impulse response, and the last 4N row elements are estimated values of impulse noise of a channel; if not, the steps S602-S605 are executed again.
9. A channel impulse response and impulse noise joint estimation device is characterized by comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a method of joint estimation of channel impulse response and impulse noise according to any one of claims 1 to 7 according to instructions in the program code.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103650442A (en) * 2011-07-14 2014-03-19 领特德国有限公司 Probe frame for single-input single-output and multi-input multi-output
EP2909951A1 (en) * 2012-10-12 2015-08-26 Sony Corporation Communications device and method for transmitting at least two parallel transmit signals
CN105812299A (en) * 2016-04-22 2016-07-27 中国地质大学（武汉） Channel estimation algorithm and system of wireless sensor network based on joint block sparse reconstruction

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103650442A (en) * 2011-07-14 2014-03-19 领特德国有限公司 Probe frame for single-input single-output and multi-input multi-output
EP2909951A1 (en) * 2012-10-12 2015-08-26 Sony Corporation Communications device and method for transmitting at least two parallel transmit signals
CN105812299A (en) * 2016-04-22 2016-07-27 中国地质大学（武汉） Channel estimation algorithm and system of wireless sensor network based on joint block sparse reconstruction

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MIMO Capacity of Class A Impulsive Noise Channel for Different Levels of Information Availability At Transmitter;Babak Nikfar等;《18th IEEE International Symposium on Power Line Communications and Its Applications》;20141231;全文 *
基于稀疏贝叶斯学习的ＭＩＭＯ电力线脉冲噪声消除;呙涛等;《电力系统自动化》;20140725;全文 *

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