Title: A blind receiver for OFDM communications

Authors: Min Lu; Min Zhang; Guangxue Yue; Bolin Ma; Wei Li

Addresses: College of Science, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China ' College of Science, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China; College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China ' College of Science, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China; College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China ' College of Data Science, Jiaxing University, Jiaxing, Zhejiang, China ' College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China

Abstract: Owing to the channel fading and noise interference in different environments, how to accurately restore the transmitted bit stream at the receiving end has become a key issue of the Orthogonal Frequency Division Multiplexing (OFDM) systems. We propose a Dual-path Mixed Deep Learning (DMDL) framework for the blind OFDM receiver, which combines the Densely connected convolutional Networks (DenseNets) and the Residual Networks (ResNets). The DMDL receiver can solve the problem of gradient explosion and feature disappearance in the network training, and it does not require pilots for the channel estimation. The experimental results show that on the Additive White Gaussian Noise (AWGN) channel, the performance of the DMDL receiver can be improved by 1.62 dB over the traditional receiver. On the Rayleigh fading channel, the performance improvement of the DMDL receiver can reach 1.94 dB. DMDL model also has excellent performance in Cyclic Prefix (CP) free and Doppler frequency shift environment.

Keywords: blind receiver; deep learning; DenseNet; OFDM; orthogonal frequency division multiplexing; ResNet; signal detection; wireless communication.

DOI: 10.1504/IJWMC.2023.131293

International Journal of Wireless and Mobile Computing, 2023 Vol.24 No.3/4, pp.203 - 216

Received: 23 Jun 2021
Accepted: 31 Dec 2021
Published online: 06 Jun 2023 *

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