An Improved Blind Channel Identification Algorithm for Sparse Multi-path Channels

Based on the idea of the effective approximation, this paper uses the matrix outer-product decomposition and proposes a new blind identification and blind equalization algorithm to solve the sparse multi-path channel (SMC) problem in the wireless communication. Firstly, an improved VIA principle is adopted to precisely estimate the order of the effective part of the SMC. Then the channel coefficient of the effective part is estimated with the improved matrix outer-product decomposition. Finally, based on the results of the estimation, the received signal is deconvoluted so that the transmitted signal is derived. In order to eliminate the interference of the noise and the zero-taps (ZT) in the channel impulse response, this paper adjusts the noise variance estimation method so that the performance of the blind identification is improved when the signal-to-noise ratio (SNR) is low. Compared to the existing method, the algorithm proposed relaxes the SNR requirement of the estimation, and overcomes the phase distortion problem in Subspace Algorithm(SSA) which is based on the LC(Liavas’ Criterion), and the noise variance estimation method in the algorithm can also be used in SNR estimation techniques. The validity of the proposed method is verified via numerical simulations and the test results on SPIB microwave channel.