Learning the Wireless Channel: A Deep Neural Network Approach

Guan-Xiong Shen, Muhammad R. A. Khandaker, Faisal Tariq

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a new deep neural network (DNN)-based channel estimation method for the Rayleigh fading channel model. While deep learning has been considered for estimating channels in many communication scenarios, direct estimation of the basic wireless single-input single-output (SISO) communication channel coefficients has not been considered. The proposed DNN-based method can efficiently estimate the channel in real time. Extensive simulation results demonstrate that the proposed channel estimator outperforms conventional least square (LS) estimators in terms of bit error rate (BER) and mean square error (MSE). In addition, the proposed channel does not need channel statistics information or complex matrix computation, thereby reducing the amount of calculation significantly.

Original languageEnglish
Title of host publication2020 International Conference on UK-China Emerging Technologies (UCET)
PublisherIEEE
ISBN (Electronic)9781728194882
DOIs
Publication statusPublished - 29 Sep 2020
Event5th International Conference on the UK-China Emerging Technologies 2020 - Glasgow, United Kingdom
Duration: 20 Aug 202021 Aug 2020

Conference

Conference5th International Conference on the UK-China Emerging Technologies 2020
Abbreviated titleUCET 2020
CountryUnited Kingdom
CityGlasgow
Period20/08/2021/08/20

Keywords

  • channel estimation
  • Deep learning
  • deep neural network
  • SISO

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems and Management
  • Engineering (miscellaneous)

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