Sum rate optimizing for multi-IRS-assisted UAV downlink transmission system using deep reinforcement learning

doi:10.19682/j.cnki.1005-8885.2024.0021

The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (5): 23-33.doi: 10.19682/j.cnki.1005-8885.2024.0021

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Sum rate optimizing for multi-IRS-assisted UAV downlink transmission system using deep reinforcement learning

Received:2023-09-25 Revised:2024-03-29 Online:2024-10-31 Published:2024-10-31
Contact: Xiao-Li He E-mail:hexiaoli_suse@hotmail.com

Abstract

Abstract:

By leveraging the high maneuverability of the unmanned aerial vehicle ( UAV) and the expansive coverage of the intelligent reflecting surface ( IRS), a multi-IRS-assisted UAV communication system aimed at maximizing the sum data rate of all users was investigated in this paper. This is achieved through the joint optimization of the trajectory and transmit beamforming of the UAV, as well as the passive phase shift of the IRS. Nevertheless, the initial problem exhibits a high degree of non-convexity, posing challenges for conventional mathematical optimization techniques in delivering solutions that are both quick and efficient while maintaining low complexity. To address this issue, a novel scheme called the deep reinforcement learning ( DRL) -based enhanced cooperative reflection network ( DCRN) was proposed. This scheme effectively identifies optimal strategies, with the long short-term memory ( LSTM) network improving algorithm convergence by extracting hidden state information. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme, manifesting substantial enhancements in sum rate and superior performance.

Key words:

intelligent reflecting surface ( IRS), unmanned aerial vehicle ( UAV) communication, deep reinforcement learning ( DRL), trajectory optimization

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Sum rate optimizing for multi-IRS-assisted UAV downlink transmission system using deep reinforcement learning

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