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Deep learning for channel tracking in IRS-assisted UAV communication systems

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journal contribution
posted on 2022-10-18, 08:02 authored by Jiadong Yu, Xiaolan LiuXiaolan Liu, Yue Gao, Chiya Zhang, Wei Zhang
To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Funding

National Key Research and Development Program of China under Grant 2020YFA0711400

Key Area Research and Development Program of Guangdong Province under Grant 2020B0101110003

Shenzhen Science and Innovation Fund under Grant JCYJ20180507182451820

Efficient signal transmission techniques for large scale antenna systems

Australian Research Council

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National Natural Science Foundation of China under Grant 62101161

History

School

  • Loughborough University London

Published in

IEEE Transactions on Wireless Communications

Volume

21

Issue

9

Pages

7711 - 7722

Publisher

Institute of Electrical and Electronics Engineers

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2022-03-14

Publication date

2022-03-25

Copyright date

2022

ISSN

1536-1276

eISSN

1558-2248

Language

  • en

Depositor

Dr Xiaolan Liu. Deposit date: 16 October 2022