Federated mmWave Beam Selection Utilizing LIDAR Data
This work aims to reduce beam selection overhead for V2I mmWave communication systems, which is an incremental improvement for the telecommunications domain.
This paper addresses the challenge of efficient beam selection in millimeter wave (mmWave) communication systems for vehicle-to-infrastructure (V2I) networks by leveraging LIDAR data. The proposed federated learning approach allows connected vehicles to collaboratively train a shared neural network using their local LIDAR data, resulting in a reduced-complexity convolutional neural network (CNN) classifier that significantly outperforms previous works in performance and complexity.
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. 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 a federated LIDAR aided beam selection method for V2I mmWave communication systems. 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 a reduced-complexity convolutional NN (CNN) classifier architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.