RIS-Assisted Unsupervised Beamforming in Internet of Vehicles

被引:0
作者
Cui, Yaping [1 ,2 ,3 ]
Wang, Gongxun [1 ,2 ,3 ]
Wu, Dapeng [1 ,2 ,3 ]
He, Peng [1 ,2 ,3 ]
Wang, Ruyan [1 ,2 ,3 ]
Liu, Yanping [4 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China
[2] Chongqing Educ Commiss China, Adv Network & Intelligent Connect Technol Key Lab, Chongqing 400065, Peoples R China
[3] Chongqing Key Lab Ubiquitous Sensing & Networking, Chongqing 400065, Peoples R China
[4] Guizhou Univ Finance & Econ, Coll Big Date Stat, Guiyang 550025, Peoples R China
基金
中国国家自然科学基金;
关键词
Array signal processing; Optimization; Reconfigurable intelligent surfaces; Reflection; Wireless communication; Signal to noise ratio; Interference; Internet of vehicles; reconfigurable intelligent surface; beamforming design; transformer; unsupervised learning; INTELLIGENT REFLECTING SURFACE; VEHICULAR COMMUNICATIONS; WIRELESS COMMUNICATION; ROBUST; MISO; TRANSMISSION; OPTIMIZATION; DESIGN; ACCESS;
D O I
10.1109/TVT.2024.3457876
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Internet of vehicles (IoVs) will require massive high data rate connections with the base station (BS) to provide promising vehicular entertainment services, such as autonomous driving and traffic management. However, vehicles often encounter obstruction from buildings while traveling in urban areas, resulting in blocked direct links between the BS and the vehicle, thereby impacting the channel quality of vehicular links. To enhance the channel capacity of the IoV, reconfigurable intelligent surface (RIS) technology is introduced to assist vehicular networking scenarios in improving the signal propagation environment. Firstly, considering the maximum transmit power budget of the BS and the phase shift constraints of the RIS, we formulate a non-convex optimization problem to maximize the channel capacity of the vehicle-to-infrastructure (V2I) links by jointly designing the active beamforming at the BS and the passive beamforming matrix at the RIS. Then, we propose a RIS-assisted unsupervised beamforming (RAUB) algorithm with a two-phase transformer network architecture to design the active beamforming and the reflection phase shift. By utilizing the global feature extraction ability of the transformer network model in the two-phase network architecture, the learning performance of the network is further improved. Simulation results demonstrate that compared with the traditional block coordinate descent (BCD) algorithm based on alternating iterative optimization, the proposed RAUB algorithm can achieve comparable performance for the sum V2I capacity with lower computational complexity and better convergence performance.
引用
收藏
页码:1385 / 1398
页数:14
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