Deep Reinforcement Learning for Interference Alignment and Power Allocation in Wireless Avionics Intra‐Communications

Wireless avionics intra‐communications (WAIC) technologies play an important role in the real‐time transmission between airborne equipment. As the number of deployed WAIC nodes increases, severe inter‐user and inter‐cabin interference occurs, degrading system performance and fairness. In this paper, a deep reinforcement learning‐based interference alignment and power allocation (DRL‐IAPA) scheme is proposed for multi‐user WAIC system. By integrating interference alignment with a deep deterministic policy gradient (DDPG) algorithm, the DRL‐IAPA scheme can mitigate interference, optimize power allocation, and ensure fairness among users under stringent latency and power constraints. Simulation results show that DRL‐IAPA improves spectral efficiency and fairness over traditional and heuristic‐based methods, and demonstrates scalability and consistent performance across various network configurations. Compared with other representative DRL algorithms, such as proximal policy optimization and soft actor–critic, our proposed DDPG‐based approach exhibits faster convergence, higher achievable reward, and better applicability in dynamic WAIC environments.