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Deep reinforcement learning based dynamic power allocation for uplink device-to-device enabled cell-free network

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conference contribution
posted on 2022-06-07, 15:24 authored by Huiyu Zhou, G Xia, Y Zhang, L Ge

In this paper, we consider device-to-device enabled uplink cell-free communication between external users with the base station. By exploiting the channel gain differences, external and cellular users are multiplexed into the transmission power domain and then non-orthogonally scheduled for transmission with the same spectrum resources. Successive interference can-cellation is then applied at the base station to decode the message signals. We introduce an effective deep reinforcement learning (DRL) scheme to optimise the worst-case user rate through the dynamic power allocation of both external and cellular users. We also compare the performance of the DRL scheme under zero-forcing beamforming and conjugate beamforming methods. Simulation results verify the effectiveness of the DRL method for guaranteeing the user fairness through the worst-case rate maximisation.

History

Author affiliation

School of Engineering, University of Leicester

Source

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSBilboa, June 15 -17, 2022, Bilbao, Spain

Version

  • AM (Accepted Manuscript)

Published in

Proceedings of IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

Publisher

IEEE

issn

2155-5052

Acceptance date

2022-05-02

Copyright date

2022

Available date

2023-08-11

Language

en

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