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

doi:10.19682/j.cnki.1005-8885.2024.0021

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (5): 23-33.doi: 10.19682/j.cnki.1005-8885.2024.0021

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

龙源¹,何小利¹,叶杨²,张博¹

1. 四川轻化工大学
2. 四川农业大学

收稿日期:2023-09-25 修回日期:2024-03-29 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者: 何小利 E-mail:hexiaoli_suse@hotmail.com

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

摘要/Abstract

摘要：

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.

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

Abstract:

Key words:

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

参考文献

[1] LU H Q, ZENG Y, JIN S, et al. Aerial intelligent reflecting surface: joint placement and passive beamforming design with 3D beam flattening. IEEE Transactions on Wireless Communications, 2021, 20(7): 4128 - 4143.

[2] DEEPAK G C, LADAS A, SAMBO Y A, et al. An overview of post-disaster emergency communication systems in the future networks. IEEE Wireless Communications, 2019, 26(6): 132 -139.

[3] SUN X F, NG D W K, DING Z G, et al. Physical layer security in UAV systems: challenges and opportunities. IEEE Wireless Communications, 2019, 26(5): 40 - 47.

[4] LI Y B, ZHANG H J, LONG K P, et al. Joint resource allocation and trajectory optimization with QoS in UAV-based NOMA wireless networks. IEEE Transactions on Wireless Communications, 2021, 20(10): 6343 - 6355.

[5] NA Z Y, LIU Y, SHI J C, et al. UAV-supported clustered NOMA for 6G-enabled Internet of things: trajectory planning and resource allocation. IEEE Internet of Things Journal, 2021, 8(20): 15041 - 15048.

[6] LI P, WANG L Y, WU W, et al. Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks. Digital Communications and Networks, 2024, 10 ( 1 ): 45 - 52.

[7] ZHONG R K, LIU X, LIU Y W, et al. Multi-agent reinforcement learning in NOMA-aided UAV networks for cellular offloading. IEEE Transactions on Wireless Communications, 2022, 21 ( 3 ): 1498 - 1512.

[8] WU Q Q, ZHANG R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394 -5409.

[9] WANG P L, FANG J, YUAN X J, et al. Intelligent reflecting surface-assisted millimeter wave communications: joint active and passive precoding design. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14960 - 14973.

[10] PANG X W, SHENG M, ZHAO N, et al. When UAV meets IRS: expanding air-ground networks via passive reflection. IEEE Wireless Communications, 2021, 28(5): 164 - 170.

[11] ZHANG X Q, ZHANG H J, DU W B, et al. IRS empowered UAV wireless communication with resource allocation, reflecting design and trajectory optimization. IEEE Transactions on Wireless Communications, 2022, 21(10): 7867 - 7880.

[12] HAN S, WANG J M, XIAO L, et al. Broadcast secrecy rate maximization in UAV-Empowered IRS backscatter communications. IEEE Transactions on Wireless Communications, 2023, 22(10): 6445 - 6458.

[13] YANG H L, XIONG Z H, ZHAO J, et al. Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications. IEEE Transactions on Wireless Communications, 2021, 20(1): 375 - 388.

[14] MEI H B, YANG K, LIU Q, et al. 3D-trajectory and phase-shift design for RIS-assisted UAV systems using deep reinforcement learning. IEEE Transactions on Vehicular Technology, 2022, 71(3): 3020 - 3029.

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

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

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