CN-122001718-A - Channel assessment method for power line carrier communication and electronic equipment

CN122001718ACN 122001718 ACN122001718 ACN 122001718ACN-122001718-A

Abstract

The application provides a channel assessment method for power line carrier communication and electronic equipment. The method comprises the steps of obtaining channel assessment frames which are sent by a signal sending end through multiple space flows and comprise time domain training signals and time domain load signals, respectively performing fast Fourier transform on the two types of time domain signals to obtain corresponding frequency domain training signals and frequency domain load signals, estimating a first channel matrix of the frequency domain training signals, calculating training signal residual errors to identify abnormal frequency domain symbols, estimating a second channel matrix and a noise covariance matrix of the effective training signals after processing the effective training signals to obtain effective training signals, constructing a channel equalization matrix based on the two matrices, respectively processing the two types of frequency domain signals to obtain training signal to interference noise ratio and load signal noise ratio of each subcarrier of each space flow, and finally weighting and fusing the two types of indexes to generate fusion signal noise ratio for assessing the channel quality of each subcarrier of each space flow. The method and the device can give consideration to the robustness and the accuracy of the power line carrier communication channel assessment.

Inventors

SUN YINJIE
LOU HONGWEI
HUANG MEIYING

Assignees

深圳智微电子科技股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260228

Claims (10)

1. A method for channel assessment for power line carrier communication, the method comprising: obtaining a channel estimation frame, wherein the channel estimation frame comprises a time domain training signal and a time domain load signal which are transmitted by a signal transmitting end through a plurality of space streams; Performing fast fourier transform on the time domain training signal and the time domain loading signal to respectively obtain a frequency domain training signal and a frequency domain loading signal, wherein the frequency domain training signal corresponding to each spatial stream comprises a plurality of repeated frequency domain symbols, different pseudo-random binary phase shift keying modulation symbols are respectively overlapped in the frequency domain symbols of different spatial streams, known pseudo-random binary phase shift keying sequences are filled in the frequency domain loading signal corresponding to each spatial stream, and the frequency domain training signal and the frequency domain loading signal are composed of a plurality of subcarriers; Estimating a first channel matrix of the frequency domain training signal, and calculating a training signal residual based on the first channel matrix to identify abnormal frequency domain symbols in the frequency domain training signal according to the training signal residual; Processing abnormal frequency domain symbols in the frequency domain training signals to obtain effective training signals, and estimating a second channel matrix and a noise covariance matrix of the effective training signals; Constructing a channel equalization matrix based on the second channel matrix and the noise covariance matrix, and respectively processing the frequency domain training signal and the frequency domain load signal based on the channel equalization matrix to obtain a training signal-to-interference-and-noise ratio and a load signal-to-noise ratio of each subcarrier of each spatial stream; and carrying out weighted fusion on the signal-to-interference-plus-noise ratio of the training signal and the signal-to-noise ratio of the load signal to obtain a fusion signal-to-noise ratio for evaluating the channel quality of each subcarrier of each spatial stream.
2. The method of claim 1, wherein said estimating a first channel matrix of said frequency domain training signal comprises: based on a locally stored known training signal, a least square estimation mode is adopted to perform channel estimation and merging noise reduction on a plurality of frequency domain symbols in the frequency domain training signal, so as to obtain a first channel matrix of the frequency domain training signal.
3. The method of claim 2, wherein said calculating a training signal residual based on said first channel matrix comprises: determining training signal estimates corresponding to a plurality of frequency domain symbols based on the first channel matrix and the known training signals; and calculating the deviation between each frequency domain symbol in the frequency domain training signals and the training signal estimation corresponding to each frequency domain symbol to obtain the training signal residual error corresponding to each frequency domain symbol.
4. The method of claim 3, wherein said identifying abnormal frequency domain symbols in said frequency domain training signal from said training signal residual comprises: constructing impact noise detection statistics corresponding to each frequency domain symbol based on the training signal residual error, and stably estimating a stable center quantity and a stable ruler measure of the impact noise detection statistics through a median; Comparing the impact noise detection statistic with the robust center quantity, the robust ruler measurement and a preset threshold coefficient; And if the absolute value of the difference between the impact noise detection statistic and the steady center quantity is larger than the product of the preset threshold coefficient and the steady ruler measure, judging that each frequency domain symbol is an abnormal frequency domain symbol.
5. The method of claim 4, wherein said processing abnormal frequency domain symbols in said frequency domain training signal comprises: Rejecting the outlier frequency domain symbols in the frequency domain training signal, or And setting weights for all frequency domain symbols in the frequency domain training signal, wherein the weights given to the abnormal frequency domain symbols are lower than the weights given to the normal frequency domain symbols.
6. The method of claim 5, wherein said estimating a second channel matrix and a noise covariance matrix of said effective training signal comprises: according to a processing mode for processing abnormal frequency domain symbols in the frequency domain training signals, processing the known training signals to obtain effective known training signals; Based on the effective known training signals, adopting a least square estimation mode to perform channel estimation and merging noise reduction on a plurality of frequency domain symbols in the effective training signals to obtain a second channel matrix of the effective training signals; And calculating the training signal residual error according to the weight of the effective training signal, and obtaining a noise covariance matrix.
7. The method of claim 6, wherein the channel equalization matrix is a minimum mean square error channel equalization matrix, wherein processing the frequency domain training signal based on the channel equalization matrix results in a training signal-to-interference-and-noise ratio for each subcarrier of each spatial stream, comprising: calculating an equalization correlation matrix based on the second channel matrix and the channel equalization matrix; calculating expected power of each subcarrier of each spatial stream and residual crosstalk power of each subcarrier of other spatial streams in the frequency domain training signal based on the equalization correlation matrix; calculating the noise power of each subcarrier of each spatial stream by combining the channel equalization matrix and the noise covariance matrix; And calculating the signal-to-interference-and-noise ratio of the training signal of each subcarrier of each spatial stream according to the expected power, the residual crosstalk power and the noise power.
8. The method of claim 7, wherein processing the frequency domain payload signal based on the channel equalization matrix to obtain a payload signal to noise ratio for each subcarrier of each spatial stream comprises: carrying out equalization processing on the frequency domain load signal based on the channel equalization matrix to obtain an equalized frequency domain load signal; Screening out subcarriers with the signal-to-interference-and-noise ratio of the training signal greater than a preset threshold as reliable subcarriers, and calculating public error estimation based on the reliable subcarriers; Carrying out common phase correction and common gain correction on the balanced frequency domain load signal based on the common error estimation to obtain a corrected frequency domain load signal; Calculating the error vector amplitude of each subcarrier of each spatial stream according to the corrected frequency domain load signal and the reference frequency domain load signal, and converting the error vector amplitude into the signal-to-noise ratio of the load signal of each subcarrier of each spatial stream, wherein the time domain load signal is obtained by performing fast Fourier transform on the reference frequency domain load signal by the signal transmitting end.
9. The method of claim 8, wherein said weighted fusion of the training signal-to-interference-plus-noise ratio and the payload signal-to-noise ratio comprises: Determining a fusion weight based on the number of frequency domain symbols in the effective training signal or the duty ratio of the abnormal frequency domain symbols, wherein the fusion weight is limited between a first set weight and a second set weight; and carrying out weighted fusion on the signal-to-interference-plus-noise ratio of the training signal and the signal-to-noise ratio of the load signal according to the fusion weight.
10. An electronic device comprising one or more processors and one or more memories, the one or more memories having stored therein at least one piece of program code that is loaded and executed by the one or more processors to implement the method of any of claims 1-9.

Description

Channel assessment method for power line carrier communication and electronic equipment Technical Field The present application relates to the field of communications technologies, and in particular, to a channel estimation method and an electronic device for power line carrier communications. Background In the communication system, the broadband carrier communication of the low-voltage power line realizes data transmission by relying on the existing power distribution network, does not need to additionally arrange a special communication line, has the remarkable advantages of low cost and wide coverage range, and has wide application prospects in the fields of smart power grids, smart home and the like. However, the original design application of the power line is electric energy transmission, but not data communication, various loads connected on the power line have diversity and time variability, and strong electromagnetic noise interference can be generated in the running process of the power grid, so that the power line channel environment is extremely bad. The power line channel has frequency selective fading, signals with different frequencies have obvious attenuation degree difference in the transmission process, and meanwhile, the power line channel has strong time variability along with complex interference forms such as narrow-band interference, pulse/impact noise and the like, the channel characteristics can be dynamically changed along with load change and the running state of a power grid. These channel defects can cause serious distortion and error code in the signal transmission process, and greatly reduce the reliability of data transmission. Based on this, before the power line carrier communication is performed, it is very necessary to accurately and robustly evaluate the channel quality, so as to provide a key basis for the communication system to select the optimal transmission parameters, configure the spatial stream and the subcarrier resources. Disclosure of Invention The embodiment of the application provides a channel assessment method, a computer program product or a computer program, a computer readable storage medium and electronic equipment for power line carrier communication, and further, the robustness and the accuracy of the power line carrier communication channel assessment can be considered at least to a certain extent. Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application. According to one aspect of the embodiment of the application, a channel assessment method for power line carrier communication is provided, and the method comprises the steps of obtaining a channel assessment frame, wherein the channel assessment frame comprises a time domain training signal and a time domain load signal which are transmitted by a signal transmitting end through a plurality of space streams; performing fast fourier transform on the time domain training signal and the time domain loading signal to respectively obtain a frequency domain training signal and a frequency domain loading signal, wherein the frequency domain training signal corresponding to each spatial stream comprises a plurality of repeated frequency domain symbols, different pseudo-random binary phase shift keying modulation symbols are respectively overlapped in the frequency domain symbols of different spatial streams, known pseudo-random binary phase shift keying sequences are filled in the frequency domain loading signal corresponding to each spatial stream, and the frequency domain training signal and the frequency domain loading signal are composed of a plurality of subcarriers; estimating a first channel matrix of the frequency domain training signal, calculating a training signal residual error based on the first channel matrix to identify abnormal frequency domain symbols in the frequency domain training signal according to the training signal residual error, processing the abnormal frequency domain symbols in the frequency domain training signal to obtain an effective training signal, estimating a second channel matrix and a noise covariance matrix of the effective training signal, constructing a channel equalization matrix based on the second channel matrix and the noise covariance matrix, processing the frequency domain training signal and the frequency domain load signal based on the channel equalization matrix respectively to obtain a training signal-to-noise ratio and a load signal-to-noise ratio of each subcarrier of each spatial stream, performing weighted fusion on the training signal-to-noise ratio and the load signal-to-noise ratio, a fused signal-to-noise ratio is obtained for evaluating the channel quality of each subcarrier for each spatial stream. In some embodiments of the present application, the estimating the first channel matrix of the frequency domain training signal based on the foregoin