| Citation: | LIU Buhua, DING Dan, YANG Liuet al. Channel compensation and signal detection of OFDM based on neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456(in Chinese)
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A method for Orthogonal Frequency Division Multiplexing (OFDM) channel compensation and signal detection based on neural network is proposed for the complex channel conditions of nonlinear distortion and multi-path effects. First, the receiver uses the Least Squares (LS) and Zero Forcing (ZF) algorithm to preprocess the data, and then the processed data are input to neural network with only one fully connected layer for further channel compensation and signal detection, and finally the data flow is recovered. The simulation results show that, without Input Back-Off (IBO), the Bit Error Rate (BER) performance of the proposed method is two orders of magnitude higher than that of LS algorithm, and one order of magnitude higher than that of Linear Minimum Mean Square Error (LMMSE) and Minimum Mean Square Error (MMSE); with IBO, the proposed method can avoid at least 4 dB power loss under LS channel estimation and at least 2 dB power loss under LMMSE and MMSE channel estimation. To some extent, this paper verifies that the new network structure of machine learning combined with prior knowledge of communication can improve the accuracy of data transmission.
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