Beamforming techniques have been widely used in the millimeter wave (mmWave) bands to mitigate the path loss of mmWave radio links as the narrow straight beams by directionally concentrating the signal energy. However, traditional mmWave beam management algorithms usually require excessive channel state information overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mmWave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE) based on its location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this paper, we present a comprehensive framework for a vision-assisted mmWave beam management system, its typical deployment scenarios as well as the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that the vision-assisted beam management is indeed attractive for next-generation wireless systems.

翻译：波束赋形技术已广泛应用于毫米波频段，通过将信号能量定向集中为窄直波束来抑制毫米波无线链路的路损。然而，传统毫米波波束管理算法通常需要过多的信道状态信息开销，导致极高的计算与通信成本，阻碍了毫米波通信的广泛部署。相比之下，在基站端采用的革命性视觉辅助波束管理系统概念，无需信道信息，仅需通过应用于视觉数据的机器学习算法确定目标用户设备的空间位置，即可为其选择最优波束。本文提出了视觉辅助毫米波波束管理系统的综合框架，阐述了其典型部署场景及框架的具体细节。进而从机器学习视角探讨了该系统面临的若干挑战及其高效解决方案。在此基础上，我们构建了新型仿真平台，可同步生成视觉与无线数据以支持模型验证与性能评估。仿真结果表明，视觉辅助波束管理对下一代无线系统确实具有显著的吸引力。