Deep Learning-based Multiuser Physical Layer Communication Without Known Channel

Jiequ Ji, Zehui Xiong, Kun Zhu, Tony Quek

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

Abstract

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

Original languageEnglish
Title of host publication2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350303582
DOIs
Publication statusPublished - 2024
Event25th IEEE Wireless Communications and Networking Conference, WCNC 2024 - Dubai, United Arab Emirates
Duration: 2024 Apr 212024 Apr 24

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Conference

Conference25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period24-04-2124-04-24

All Science Journal Classification (ASJC) codes

  • General Engineering

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