Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications.
Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework
Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth（电子与计算机工程系，马萨诸塞大学达特茅斯分校）；Department of Graduate Computer Science and Engineering, Katz School of Science and Health, Yeshiva University（研究生计算机科学与工程系，耶鲁大学科学与健康学院）
Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attacks
Electrical and Computer Engineering Department, Worcester Polytechnic Institute, USA（沃斯特理工学院电子与计算机工程系）；Electrical and Computer Engineering Department, Mississippi State University, USA（密苏里州立大学电子与计算机工程系）
