Channel and asynchronous impulse noise joint estimation method in OFDM (Orthogonal Frequency Division Multiplexing) system

Abstract

The invention discloses a joint estimation method for channel and asynchronous impulse noise in an OFDM (Orthogonal Frequency Division Multiplexing) system. The joint estimation method comprises the following steps: firstly, regarding channel impulse response and impulse noise as an unknown sparse vector; then, a joint channel and impulse noise suppression algorithm based on sparse Bayesian learning (SBL) and forward-backward Kalman filtering (FB-Kalman) is put forward; finally, estimating unknown parameters of the dynamic linear system by using expectation maximization (EM), solving a mean value and a covariance matrix of the step E by using an FB-Kalman algorithm, and iteratively estimating the unknown parameters by using the step M; the method has the advantages that the channel and the asynchronous impulse noise can be jointly estimated based on the compressed sensing technology and the Kalman filtering technology, the estimation accuracy is high, and the simulation result shows that the channel estimation and bit error rate performance of the OFDM system under the asynchronous impulse noise are improved.

Description

technical field [0001] The present invention relates to a joint estimation technique of channel and impulse noise, in particular to a joint estimation method of channel and asynchronous impulse noise in an OFDM (Orthogonal Frequency Division Multiplexing, Orthogonal Frequency Division Multiplexing) system, which is based on compressed sensing technology and Kalman filter technique. Background technique [0002] In several applications of wireless communication technologies, such as vehicle networks, smart grids, and shallow sea underwater networks, the transmission of data signals will be severely degraded by impulse noise (IN). There are various sources of impulse noise, such as automobile ignition noise, electrical equipment switching, various offshore operations, etc. Compared with additive white Gaussian noise (AWGN), impulse noise has the characteristics of random generation, short duration and high impulse power. [0003] Orthogonal Frequency Division Multiplexing (O...

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Problems solved by technology

However, this method has the following problems: 1) Although the nonlinear estimator of impulse noise can improve the SNR of OFDM receivers by setting multiple thresholds, it requires noise prior statistics to obtain the optimal threshold, however When the noise prior statistics do not match the time-varying noise statistics, the performance will drop, and the noise prior statistics are not easy to obtain in reality; 2) The designed memoryless nonlinear preprocessor may destroy the OFDM reception Orthogonality between subcarriers in the machine, which leads to intercarrier interference in the frequency domain

Although compressive sensing-based impulsive noise mitigation methods have clear advantages over methods based on memoryless nonlinear preconditioners, the performance of compressive sensing-based impulsive noise mitigation methods is mainly limited by the number of empty subcarriers

The performance of nonlinear estimators for impulse noise can be improved by increasing the number of empty subcarriers, however, having more empty subcarriers means lower throughput

When the number of empty subcarriers is limited, it is expected to utilize the information available in all subcarriers to improve the estimation performance of impulse noise, however, the difficulty of utilizing all subcarriers is how to estimate channel and impulse noise simultaneously

Considering the joint estimation of all subcarriers, some researchers use the sparse Bayesian learning method to jointly estimate the channel and impulse noise for each OFDM symbol. This method jointly estimates the two, but does not use the The correlation of the time-varying channel, the estimation accuracy needs to be improved

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