Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation

TL;DR

This paper introduces an explainable AI framework for feature selection and model simplification in deep reinforcement learning for V2X resource management, improving efficiency while maintaining high performance.

Contribution

It presents a novel two-stage XAI-based approach using SHAP to reduce model complexity and enhance interpretability in MADRL for V2X communications.

Findings

Achieves 97% of original MADRL sum-rate performance.

Reduces state features by 28%.

Cuts training time by 11% and parameters by 46%.

Abstract

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 input. Simulation results demonstrate that the XAI-assisted methodology achieves 97% of the original MADRL sum-rate performance while reducing optimal state features by 28%, average training time by 11%, and trainable weight parameters by 46% in a network with eight vehicular pairs.