Simulation of High-precision Estimation Methods for Time-varying Communication Sparse Channels
The environment of wireless channel is becoming increasingly complex,and the channel quality directly affects the signal processing performance and system communication ability of receivers.Therefore,an optimization strategy for estimation algorithm of time-varying communication channel based on compressed sensing was proposed.Firstly,we calculated the signal-to-noise ratio of the received subcarrier signal,and then used dynamic subchannel selection method to detect signal features.Secondly,we calculated the average tap gain of the orthogonal frequency di-vision multiplexing symbol period.Thirdly,we used the periodic prefix mode to eliminate the multi-channel inter-symbol interference.Moreover,we constructed a frequency-domain transfer model of time-varying communication channel,thus clarifying the state of time-varying communication.Furthermore,we used the compressed sensing algo-rithm to calculate the sparsity features of signal.Meanwhile,we used step factor parameters to control the sparsity and implemented the subspace iterative tracking,thus searching for the most appropriate sparsity atoms of input signal.Fi-nally,we used the fuzzy thresholds to optimize atomic features and reconstruct the initial signal,thus obtaining high-precision estimation results.The simulation results show that the proposed algorithm has small estimation error of com-munication channel and can provide reference for reliable applications in wireless communication.
Compressed sensingTime-varying communicationChannel estimationMatching pursuitSignal-to-noise ratio
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