Explainable AI-Aided Feature Selection and Model Reduction for DRL-Based V2X Resource Allocation

Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model's input. Simulation results demonstrate that the XAI-assisted methodology achieves similar to 97% of the original MADRL sum-rate performance while reducing optimal state features by similar to 28%, average training time by similar to 11%, and trainable weight parameters by similar to 46% in a network with eight vehicular pairs.