Smart Vehicular Communication via 5G mmWaves

Millimeter-Wave (mmWave) communication is conceived as a viable approach for 5G vehicular communication systems, where vehicles are equipped with more sensors that generate Gbps data for future autonomous driving. However, such directional mmWave communication relies on accurate beam alignment and is sensitive to blockage. Dense deployment of mmWave base stations (mmBSs) and high mobility of vehicles also lead to frequent handovers and complex beam alignment calculation. 5G mmWave vehicular communication calls for a smart and stable solution. To this end, we propose an online learning scheme to address the problem of beam selection with blockage-free guarantee in 5G mmWave vehicular networks. We first model this problem as a contextual combinatorial multi-armed bandit (MAB) problem with QoS constraints and delayed feedback. Next, we propose an online learning algorithm, BPG, to predict beam directions, with provable sub-linear regret and blockage-free bounds. BPG exploits the context space and learns the expected weight of each beam from arrived vehicles’ contexts and the delayed feedback. To validate the efficiency of BPG, we also conduct trace-driven simulations based on real-world traffic patterns. Simulation results show that BPG achieves close-to-optimal throughput with low violation and outperforms other benchmark algorithms.