Channel Estimation Algorithm of OFDM System Based on GSA-BP Neural Network

doi:10.12068/j.issn.1005-3026.2022.06.002

Abstract

Abstract: The nonlinear noise problem in orthogonal frequency division multiplexing(OFDM)system cannot be ignored. In order to understand the channel characteristics better， channel state information is needed by channel estimation. A channel estimation algorithm for OFDM system was proposed based on golden sine algorithm optimized BP neural network(GSA-BP). The problem was overcome that the traditional BP neural network algorithm is easy to fall into local extremum， and the estimation accuracy of channel estimation algorithm was improved. Firstly， the initial estimation of the channel was obtained through the LS channel estimation algorithm. Then， the accurate estimation of channel was obtained by using GSA-BP neural network. Simulation results show that the proposed algorithm has better performance than LS algorithm， and is close to MMSE algorithm， but it does not need channel prior statistics and is easy to implement.

Key words: golden sine algorithm; BP neural network; OFDM(orthogonal frequency division multiplexing)system; least square algorithm; channel estimation

CLC Number:

TN911.7

JI Ce， ZHANG Xiao. Channel Estimation Algorithm of OFDM System Based on GSA-BP Neural Network[J]. Journal of Northeastern University(Natural Science), 2022, 43(6): 769-775.

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