  In this letter, an age of information (AoI)-aware transmission power and
resource block (RB) allocation technique 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 threshold 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 actively learn the network dynamics, estimate the vehicles' future
AoI, and proactively allocate resources. Simulation results show a significant
improvement in terms of AoI violation probability, compared to several
baselines, with a reduction of at least 50%.
