A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System

ZHONG Weizhi; WAN Shiqing; DUAN Hongtao; FAN Zhenxiong; LIN Zhipeng; HUANG Yang; MAO Kai

doi:10.11999/JEIT240561

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

ZHONG Weizhi, WAN Shiqing, DUAN Hongtao, FAN Zhenxiong, LIN Zhipeng, HUANG Yang, MAO Kai. A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240561

Citation:

ZHONG Weizhi, WAN Shiqing, DUAN Hongtao, FAN Zhenxiong, LIN Zhipeng, HUANG Yang, MAO Kai. A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240561

Citation:

ZHONG Weizhi, WAN Shiqing, DUAN Hongtao, FAN Zhenxiong, LIN Zhipeng, HUANG Yang, MAO Kai. A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240561

PDF( 2072 KB)

A Joint Beamforming Method Based on Cooperative Co-evolutionary in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle Communication System

doi: 10.11999/JEIT240561

1.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
State Radio Monitoring Center, Beijing 100144, China

Funds: The National Natural Science Foundation of China (62271250), The Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry) (BE2022067, BE2022067-1, BE2022067-3), The Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

Received Date: 2024-07-04
Rev Recd Date: 2024-11-07

Available Online: 2024-11-13

Abstract

Abstract

Objective: High-quality wireless communication enabled by Unmanned Aerial Vehicles (UAVs) is set to play a crucial role in the future. In light of the limitations posed by traditional terrestrial communication networks, the deployment of UAVs as nodes within aerial access networks has become a vital component of emerging technologies in Beyond Fifth Generation (B5G) and sixth generation (6G) communication systems. However, the presence of infrastructure obstructions, such as trees and buildings, in complex urban environments can hinder the Line-of-Sight (LoS) link between UAVs and ground users, leading to a significant degradation in channel quality. To address this challenge, researchers have proposed the integration of Reconfigurable Intelligent Surfaces (RIS) into UAV communication systems, providing an energy-efficient and flexible passive beamforming solution. RIS consists of numerous adjustable electromagnetic units, with each element capable of independently configuring various phase shifts. By adjusting both the amplitude and phase of incoming signals, RIS can intelligently reflect signals from multiple transmission paths, thereby achieving directional signal enhancement or nulling through beamforming. Given the limitations of conventional joint beamforming methods—such as their exclusive focus on optimizing the RIS phase shift matrix and lack of universality—a novel joint beamforming approach based on a Cooperative Co-Evolutionary Algorithm (CCEA) is proposed. This method aims to enhance Spectrum Efficiency (SE) in multi-user scenarios involving RIS-assisted UAV communications. Methods: The proposed approach begins by optimizing the RIS phase shift matrix, followed by the design of the beam shape for RIS-reflected waves. This process modifies the spatial energy distribution of RIS reflections to improve the Signal-to-Interference-plus-Noise Ratio (SINR) at the receiver. To address challenges in existing optimization algorithms, an Evolutionary Algorithm (EA) is introduced for the first time, and a cooperative co-evolutionary structure based on EA is developed to decouple joint beamforming subproblems. The central concept of CCEA revolves around decomposing complex problems into several subproblems, which are then solved through distributed parallel evolution among subpopulations. The evaluation of individuals within each subpopulation, representing solutions to their respective subproblems, relies on collaboration among different populations. Specifically, this involves merging individuals from one subpopulation with representative individuals from others to create composite solutions. Subsequently, the overall fitness of these composite solutions is assessed to evaluate individual performance within each subpopulation. Results and Discussions: The simulation results demonstrate that, in comparison to joint beamforming, which focuses solely on designing the RIS phase shift matrix, further optimizing the shape of the reflected beam from the RIS significantly enhances the accuracy and effectiveness of the main lobe coverage over the user's position, resulting in improved SE. Although Maximum Ratio Transmission (MRT) precoding can maximize the output SINR of the desired signal, it may also lead to considerable inter-user interference, which subsequently diminishes the SE. Therefore, the implementation of joint beamforming is essential. The optimization algorithms proposed in this paper are effective for both the actual amplitude-phase shift model and the ideal RIS amplitude-phase shift model. However, factors such as dielectric loss associated with the actual circuit structure of the RIS can attenuate the strength of the reflected wave reaching the client, thereby reducing the SINR at the receiving end and ultimately lowering the SE. Additionally, the increase in SE achievable through Deep Reinforcement Learning (DRL) and Alternating Optimization (AO) is limited when compared to CCEA. Unlike the optimization of individual action strategies employed in DRL, the CCEA algorithm produces a greater variety of solutions by utilizing crossover and mutation among individuals within the population, thereby mitigating the risk of local optimization. Moreover, CCEA can optimize the spatial distribution of the reflected waves through a more sophisticated design of the RIS reflecting beam shape. This results in an enhanced signal intensity at the receiving end, allowing for a higher SE compared to AO and DRL, which primarily focus on optimizing the RIS phase shift matrix. Conclusions: In light of the limitations observed in previous joint beamforming optimization methods, this paper introduces a novel joint beamforming optimization approach based on CCEA. This method effectively decomposes the joint beam optimization problem into two distinct sub-problems: the design of the RIS reflection beam waveform and the beamforming design at the transmitter. These sub-problems are addressed through independent parallel evolution, utilizing two separate sub-populations. Notably, for RIS passive beamforming, this approach innovatively optimizes the RIS phase shift matrix alongside the design of the RIS reflected beam shape for the first time. Numerical simulation results indicate that, compared to joint beamforming strategies that focus solely on optimizing the RIS phase shift matrix, a more meticulous design of the RIS reflected waveform can significantly alter the intensity distribution of reflected waves in 3D space. This alignment enables the reflected beam to converge on the user's location while mitigating interference, thereby enhancing the system's SE. Furthermore, the CCEA algorithm demonstrates the capability to achieve effective coverage of RIS reflected beams for users, regardless of varying base station and user locations. The optimization process leads to a reduction in Peak Side Lobe Level (PSLL) and an improvement in SE by at least 5 dB, showing its spatial applicability across diverse scenarios. Future research will aim to further investigate the application of evolutionary algorithms and swarm intelligence optimization techniques in joint beamforming optimization, as well as explore the potential of RIS beam waveform design to optimize communication systems, adapting to increasingly complex and diversified communication requirements.

FullText(HTML)

References(17)

References

[1]	ZENG Yong, ZHANG Rui, and LIM T J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36–42. doi: 10.1109/MCOM.2016.7470933.
[2]	朱秋明, 华博宇, 毛开, 等. 无人机毫米波信道建模进展和挑战[J]. 数据采集与处理, 2020, 35(6): 1049–1059. doi: 10.16337/j.1004-9037.2020.06.004. ZHU Qiuming, HUA Boyu, MAO Kai, et al. Advances and challenges of UAV millimeter-wave channel modeling[J]. Journal of Data Acquisition and Processing, 2020, 35(6): 1049–1059. doi: 10.16337/j.1004-9037.2020.06.004.
[3]	朱秋明, 倪浩然, 华博宇, 等. 无人机毫米波信道测量与建模研究综述[J]. 移动通信, 2022, 46(12): 2–11. doi: 10.3969/j.issn.1006-1010.20221114-0001. ZHU Qiuming, NI Haoran, HUA Boyu, et al. A survey of UAV millimeter-wave channel measurement and modeling[J]. Mobile Communications, 2022, 46(12): 2–11. doi: 10.3969/j.issn.1006-1010.20221114-0001.
[4]	PANG Xiaowei, SHENG Min, ZHAO Nan, et al. When UAV meets IRS: Expanding air-ground networks via passive reflection[J]. IEEE Wireless Communications, 2021, 28(5): 164–170. doi: 10.1109/MWC.010.2000528.
[5]	WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025.
[6]	MA Dong, DING Ming, and HASSAN M. Enhancing cellular communications for UAVs via intelligent reflective surface[C]. 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020: 1–6. doi: 10.1109/WCNC45663.2020.9120632.
[7]	LIU Xin, YU Yingfeng, PENG Bao, et al. RIS-UAV enabled worst-case downlink secrecy rate maximization for mobile vehicles[J]. IEEE Transactions on Vehicular Technology, 2023, 72(5): 6129–6141. doi: 10.1109/TVT.2022.3231376.
[8]	NA Zhenyu, LIU Yue, SHI Jingcheng, et al. UAV-supported clustered NOMA for 6G-enabled internet of things: Trajectory planning and resource allocation[J]. IEEE Internet of Things Journal, 2021, 8(20): 15041–15048. doi: 10.1109/JIOT.2020.3004432.
[9]	NA Zhenyu, JI Chenglan, LIN Bin, et al. Joint optimization of trajectory and resource allocation in secure UAV relaying communications for internet of things[J]. IEEE Internet of Things Journal, 2022, 9(17): 16284–16296. doi: 10.1109/JIOT.2022.3151105.
[10]	YE Jia, QIAO Jingping, KAMMOUN A, et al. Nonterrestrial communications assisted by reconfigurable intelligent surfaces[J]. Proceedings of the IEEE, 2022, 110(9): 1423–1465. doi: 10.1109/JPROC.2022.3169690.
[11]	HUANG Chongwen, MO Ronghong, and YUEN C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1839–1850. doi: 10.1109/JSAC.2020.3000835.
[12]	万诗晴, 仲伟志, 何艺, 等. 基于深度强化学习的可重构智能超表面辅助无人机通信联合波束成形与轨迹优化[J]. 电波科学学报, 2024, 39(4): 722–731. doi: 10.12265/j.cjors.2023233. WAN Shiqing, ZHONG Weizhi, HE Yi, et al. The optimization of beamforming and trajectory for reconfigurable intelligent surface assisted UAV communication system based on deep reinforcement learning[J]. Chinese Journal of Radio Science, 2024, 39(4): 722–731. doi: 10.12265/j.cjors.2023233.
[13]	DANG Jian, ZHANG Zaichen, LI Yewei, et al. Fast and arbitrary beam pattern design for RIS-assisted terahertz wireless communication[J]. IEEE Transactions on Vehicular Technology, 2023, 72(2): 2620–2625. doi: 10.1109/TVT.2022.3209669.
[14]	RAJAGOPALAN H and RAHMAT-SAMII Y. Loss quantification for microstrip reflectarray: Issue of high fields and currents[C]. 2008 IEEE Antennas and Propagation Society International Symposium, San Diego, USA, 2008: 1–4. doi: 10.1109/APS.2008.4619755.
[15]	ABEYWICKRAMA S, ZHANG Rui, WU Qingqing, et al. Intelligent reflecting surface: Practical phase shift model and beamforming optimization[J]. IEEE Transactions on Communications, 2020, 68(9): 5849–5863. doi: 10.1109/TCOMM.2020.3001125.
[16]	SAREMI S, MIRJALILI S, and LEWIS A. Biogeography-based optimisation with chaos[J]. Neural Computing and Applications, 2014, 25(5): 1077–1097. doi: 10.1007/s00521-014-1597-x.
[17]	何艺, 仲伟志, 万诗晴, 等. 智能反射面辅助的MU-MISO车联网毫米波通信联合波束赋形[J]. 信号处理, 2024, 40(2): 336–344. doi: 10.16798/j.issn.1003-0530.2024.02.011. HE Yi, ZHONG Weizhi, WAN Shiqing, et al. Joint beamforming for IRS-aided MU-MISO millimeter wave communication of vehicular network[J]. Journal of Signal Processing, 2024, 40(2): 336–344. doi: 10.16798/j.issn.1003-0530.2024.02.011.