User Identification and Channel Estimation by Iterative DNN-Based Decoder on Multiple-Access Fading Channel

Lantian WEI
Shan LU
Hiroshi KAMABE
Jun CHENG

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E105-A    No.3    pp.417-424
Publication Date: 2022/03/01
Publicized: 2021/09/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2021TAP0008
Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)
Category: Communication Theory and Signals
Keyword: 
signature code,  deep neural network,  compressed sensing,  user identification,  channel estimation,  

Full Text: FreePDF(2.1MB)

Summary: 
In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.


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