  In this paper, we investigate the problem of age of information (AoI)-aware
radio resource management for expected long-term performance optimization in a
Manhattan grid vehicle-to-vehicle network. With the observation of global
network state at each scheduling slot, the roadside unit (RSU) allocates the
frequency bands and schedules packet transmissions for all vehicle user
equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure
as a discrete-time single-agent Markov decision process (MDP). The technical
challenges in solving the optimal control policy originate from high spatial
mobility and temporally varying traffic information arrivals of the VUE-pairs.
To make the problem solving tractable, we first decompose the original MDP into
a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on
long short-term memory and deep reinforcement learning techniques to address
the partial observability and the curse of high dimensionality in local network
state space faced by each VUE-pair. With the proposed algorithm, the RSU makes
the optimal frequency band allocation and packet scheduling decision at each
scheduling slot in a decentralized way in accordance with the partial
observations of the global network state at the VUE-pairs. Numerical
experiments validate the theoretical analysis and demonstrate the significant
performance improvements from the proposed algorithm.
