Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

Abstract--In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation tech nique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined thre shold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to a ctively learn the network dynamics, estimate the vehicles' future A oI, and proactively allocate resources. Simulation results sh ow a significant improvement in terms of AoI violation probabili ty, compared to several baselines, with a reduction of at least 50%.