Optical Intelligent Reflecting Surfaces-Assisted Distributed OMC for UAV Clusters
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摘要: 随着无人机(UAV)系统的规模持续扩大以及对更高通信速率的需求增长，UAV光移动通信(UAV-OMC)已经成为一个有前景的技术方向。然而，传统的UAV-OMC难以支持多UAV之间的通信。该文基于光学智能反射表面(OIRS)技术，提出一个适用于UAV群的分布式OMC系统。通过在特定的UAV上设置OIRS，利用OIRS将光信号从单个UAV节点扩散到多个UAV节点。这一系统在保留UAV-OMC系统的高能效和高速度的同时，能够支持分布式UAV群的通信。对所提出的系统进行了数学建模，考虑了一系列现实因素，如OIRS的光束控制、UAV之间的相对运动和UAV的抖动等，这些因素都符合实际系统的特点。此外，该文还推导出了系统的误比特率(BER)和渐进中断概率的闭式表达式。基于理论分析和模拟结果，讨论了各个参数和系统设计的影响。Abstract: As the scale of Unmanned Aerial Vehicle (UAV) systems and the demand for higher communication rates continue to grow, UAV Optical Mobile Communications (UAV-OMC) has emerged as a promising technical direction. However, it is difficult for traditional UAV-OMC to support multiple UAVs’ communications. In this paper, based on the Optical Intelligent Reflecting Surface (OIRS) technology, we propose a distributed OMC system for UAV clusters. By setting the OIRS on a specific UAV, we utilize OIRS to spread the optical signal from a single UAV node to multiple UAV nodes. While retaining the high energy efficiency and high speed of the UAV-OMC system, this system can support the communication of distributed UAV clusters. This paper conducts mathematical modeling of the proposed system. When modeling the system, we took into account a series of realistic factors, such as OIRS beam control, relative motion between UAVs, UAV jitter, which fit the actual system. Closed-form expressions for the system's Bit Error Rate (BER) and asymptotic outage probability are also derived. Based on theoretical analysis and simulation results, the effect of each parameter and system design have been discussed.
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表 1 系统参数
参数 值 光波长 ($\lambda $) 1550 nm接收机的噪声方差($ \sigma _n^2 $) ${10^{ - 6}}$ 发射端发散角度($ \phi $) 6 mrad 发射端抖动标准差($ {\sigma _{{\varphi _{{t_s}}}}} $) $2 \times {10^{ - 3}}$ 从发射端到OIRS的链路距离($ {l_{s,o}} $) 100 m 大气衰减系数(${\iota _n}$) 0.9 OIRS到从属UAV n的链路距离($ {l_{o,{r_n}}} $) 50 m 接收机直径 (2a) 20 cm 
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[1] 张在琛, 江浩. 智能超表面使能无人机高能效通信信道建模与传输机理分析[J]. 电子学报, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.ZHANG Zaichen and JIANG Hao. Channel modeling and characteristics analysis for high energy-efficient RIS-assisted UAV communications[J]. Acta Electronica Sinica, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352. [2] 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. [3] DABIRI M T, SADOUGH S M S, and ANSARI I S. Tractable optical channel modeling between UAVs[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11543–11550. doi: 10.1109/TVT.2019.2940226. [4] ZHANG Zaichen, DANG Jian, WU Liang, et al. Optical mobile communications: Principles, implementation, and performance analysis[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 471–482. doi: 10.1109/TVT.2018.2880817. [5] NAJAFI M, SCHMAUSS B, and SCHOBER R. Intelligent reflecting surfaces for free space optical communication systems[J]. IEEE Transactions on Communications, 2021, 69(9): 6134–6151. doi: 10.1109/TCOMM.2021.3084637. [6] JAMALI V, AJAM H, NAJAFI M, et al. Intelligent reflecting surface assisted free-space optical communications[J]. IEEE Communications Magazine, 2021, 59(10): 57–63. doi: 10.1109/MCOM.001.2100406. [7] WANG Haibo, ZHANG Zaichen, ZHU Bingcheng, et al. Approaches to array-type optical IRSs: Schemes and comparative analysis[J]. Journal of Lightwave Technology, 2022, 40(12): 3576–3591. doi: 10.1109/JLT.2022.3152812. [8] MING Rui, ZHOU Zhiyan, LUO Xiwen, et al. Optical tracking system for multi-UAV clustering[J]. IEEE Sensors Journal, 2021, 21(17): 19382–19394. doi: 10.1109/JSEN.2021.3091280. [9] DABIRI M T, REZAEE M, MOHAMMADI L, et al. Modulating retroreflector based free space optical link for UAV-to-ground communications[J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8631–8645. doi: 10.1109/TWC.2022.3167945. [10] NATH S, SENGAR S, SHRIVASTAVA S K, et al. Impact of atmospheric turbulence, pointing error, and traffic pattern on the performance of cognitive hybrid FSO/RF system[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(4): 1194–1207. doi: 10.1109/TCCN.2019.2952116. [11] SANDALIDIS H G, TSIFTSIS T A, KARAGIANNIDIS G K, et al. BER performance of FSO links over strong atmospheric turbulence channels with pointing errors[J]. IEEE Communications Letters, 2008, 12(1): 44–46. doi: 10.1109/LCOMM.2008.071408. [12] IJAZ M, GHASSEMLOOY Z, PEREZ J, et al. Enhancing the atmospheric visibility and fog attenuation using a controlled FSO channel[J]. IEEE Photonics Technology Letters, 2013, 25(13): 1262–1265. doi: 10.1109/LPT.2013.2264046. [13] SUN Shiyuan, WANG Tengjiao, YANG Fang, et al. Intelligent reflecting surface-aided visible light communications: Potentials and challenges[J]. IEEE Vehicular Technology Magazine, 2022, 17(1): 47–56. doi: 10.1109/MVT.2021.3127869. [14] AJAM H, NAJAFI M, JAMALI V, et al. Modeling and design of IRS-assisted multilink FSO systems[J]. IEEE Transactions on Communications, 2022, 70(5): 3333–3349. doi: 10.1109/TCOMM.2022.3163767. [15] WANG Zhengdao and GIANNAKIS G B. A simple and general parameterization quantifying performance in fading channels[J]. IEEE Transactions on Communications, 2003, 51(8): 1389–1398. doi: 10.1109/TCOMM.2003.815053. -
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