  This paper studies the problem of minimizing the age of information (AoI) in
cellular vehicle-to-everything communications. To provide minimal AoI and high
reliability for vehicles' safety information, NOMA is exploited. We reformulate
a resource allocation problem that involves half-duplex transceiver selection,
broadcast coverage optimization, power allocation, and resource block
scheduling. First, to obtain the optimal solution, we formulate the problem as
a mixed-integer nonlinear programming problem and then study its NP-hardness.
The NP-hardness result motivates us to design simple solutions. Consequently,
we model the problem as a single-agent Markov decision process to solve the
problem efficiently using fingerprint deep reinforcement learning techniques
such as deep-Q-network (DQN) methods. Nevertheless, applying DQN is not
straightforward due to the curse of dimensionality implied by the large and
mixed action space that contains discrete and continuous optimization
decisions. Therefore, to solve this mixed discrete/continuous problem
efficiently, simply and elegantly, we propose a decomposition technique that
consists of first solving the discrete subproblem using a matching algorithm
based on state-of-the-art stable roommate matching and then solving the
continuous subproblem using DRL algorithm that is based on deep deterministic
policy gradient DDPG. We validate our proposed method through Monte Carlo
simulations where we show that the decomposed matching and DRL algorithm
successfully minimizes the AoI and achieves almost 66% performance gain
compared to the best benchmarks for various vehicles' speeds, transmission
power, or packet sizes. Further, we prove the existence of an optimal value of
broadcast coverage at which the learning algorithm provides the optimal AoI.

Networking and Internet Architecture