Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection on the network performance. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose distributed LIDAR aided beam selection for V2I mmWave communication systems utilizing federated training. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system. We also propose an alternative reduced-complexity convolutional NN (CNN) architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.

翻译：毫米波(mmWave)通信系统的有效连接配置是一项关键但具有挑战性的任务,因为光束选择对网络性能造成管理费用。对于车辆到基础设施(V2I)网络来说,安装在车辆上的LIDAR传感器的侧边信息已被利用来减少光束搜索管理费用。我们在信中提议利用联合培训,为V2ImmWave通信系统分发LIDAR辅助光束选择。在拟议计划中,连接车辆在系统正常运行期间合作对共享神经网络进行当地可用的LIDAR数据的培训。我们还提议采用另一种降低复合性NNN(CNN)结构和LIDAR预处理方法,这在性能和复杂性方面大大超过以往的工作。