Context-and-Social-Aware Online Beam Selection for mmWave Vehicular Communications

Abstract

Millimeter-wave (mmWave) bands are expected to be an important choice for future vehicular communication to support Gbps links for reliable data transfer in high-rate applications. The recent online learning technologies addressed the problem of fast beam tracking by exploiting user location information and mining received data in mmWave vehicular systems to adapt to the vehicle's environmental situation. However, the fairness and efficiency over mmWave beams are difficult to maintain on the move, especially for high-density traffic, since the number of available beams is quite limited by hardware and cost for current antenna arrays. Fortunately, the social structure of preferences between the neighboring smart cars and their passengers can be leveraged to improve the beam coverage efficiency by performing the broadcast transmission via a single beam. In this article, we propose a double-layer online learning algorithm, namely, context- and social-aware machine learning (CSML), that is based on the context and social preference information of vehicles and passengers, to realize fast beam access with broadcast coverage in mmWave communication systems. Based on the multiarmed bandit model, CSML embodies the selection of appropriate beams in the first layer and steers the broadcast angle along these beams in the second layer by aggregating the received data. Furthermore, CSML needs to adjust the timing of exploration and exploitation based on the social information, i.e., the probability of vehicles meeting with each other that have the same preference. Finally, we perform an extensive evaluation using realistic traffic patterns and show that CSML increases the efficiency of mmWave base stations by using social data and can achieve near-optimal system performance.