Adaptive Resource Allocation in Covert V2X Systems via Multi-Agent DRL

This letter investigates adaptive resource allocation in a covert Vehicle-to-Everything (V2X) communication system comprising a Vehicle-to-Vehicle (V2V) pair, multiple wardens, and multiple cellular vehicular users. The goal is to maximize total system capacity while satisfying covertness, power, bandwidth, and latency constraints. The optimal detection threshold and total detection error probability for the warden are first derived analytically. To solve the resulting constrained optimization problem, a decentralized Adaptive Constraint-Aware Multi-Agent Deep Deterministic Policy Gradient (ACA-MADDPG) framework is proposed. Each agent independently learns transmit strategies using local observations and constraint feedback, enabling scalable deployment in dynamic environments. Simulation results verify that the proposed method outperforms DQN- and DDPG-based benchmarks in both capacity and reward convergence.