A Model-driven Deep Learning Signal Processing Scheme for OFDM System
Authors: 
Jun Li, Wenxin Li, Bo He, Tongliang Xin, Fei LinAuthors Info & Claims
Jun Li, Wenxin Li, Bo He, Tongliang Xin, Fei LinAuthors Info & ClaimsAbstract
Deep learning (DL) has received more attention in physical layer communication. Channel estimation (CE) and signal detection (SD) constitute the key modules within the signal processing chain of orthogonal frequency-division multiplexing (OFDM) communication receivers. In this paper, we propose a model-driven deep learning scheme for the OFDM receiver, known as CSNet, which contains two modules, CE module and SD module. This structure maintains the block by block signal processing form in the OFDM system. In addition, we introduce the traditional linear algorithms minimum mean-squared error (LMMSE) and zero-forcing (ZF) to initialize the neural network of two modules, respectively, which improve the training speed of the sub-network and the generalization of the model. Compared with the traditional scheme and data-driven deep learning scheme, the model-driven DL receiver provides a more accurate channel estimation property. Simulation experiments demonstrate that the proposed scheme is robust in bit error ratio (BER) performance especially in the nonlinear case.
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March 2022
237 pages
ISBN:9781450387415
DOI:10.1145/3531232
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Published: 01 June 2022
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IVSP 2022
IVSP 2022: 2022 4th International Conference on Image, Video and Signal Processing
March 18 - 20, 2022
Singapore, Singapore
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