Machine Learning Enabling Analog Beam Selection for Concurrent Transmissions in Millimeter-Wave V2V Communications

Abstract

With the development of millimeter-wave (mmWave) technology and vehicle-to-vehicle (V2V) communications, the mmWave vehicular ad hoc networks (VANETs) is envisioned to support rapidly growing number of vehicles. Against this background, each V2V user (VUE) is expected to employ large-scale array to form directional analog beams for improving spatial spectrum reuse, and it is capable of achieving concurrent transmissions from multiple other VUEs simultaneously. However, due to the high dynamics of V2V links, it can be challenging for each VUE to quickly select an effective analog beam. In this paper, we propose a machine learning (ML) approach to achieve an efficient and fast analog beam selection for mmWave V2V communications. Specifically, we first derive the probabilities that multiple V2V transmitters (TXs) serve one VUE to obtain the average sum rate (ASR) for mmWave V2V communications. On that basis, we develop an ML approach to maximize the ASR, whereby the support vector machine (SVM) classifier is utilized for optimizing the analog beam selection. Besides, we further proposed an iteration sequential minimal optimization training algorithm to train data samples of all V2V links, and convergence of the proposed solution is also discussed. Finally, an extensive sample training and simulations are evaluated by Google TensorFlow. The results verified that our proposed ML approach is capable of achieving a higher ASR yet substantially lower computational complexity than traditional solutions based on explicitly estimated channels.