Federated mmWave Beam Selection Utilizing LIDAR Data

• URL: http://arxiv.org/abs/2102.02802v1
• Date: Thu, 4 Feb 2021 18:49:20 GMT
• Title: Federated mmWave Beam Selection Utilizing LIDAR Data
• Authors: Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung, Szymon Kobus, and Deniz Gunduz
• Abstract summary: 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.
• Score: 3.7352534957395522
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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