Optimizing Fairness and Spectral Efficiency With Shapley-Based User Prioritization in Semantic Communication

Abstract

Efficient and fair resource allocation is a critical challenge in modern semantic communication systems, particularly in scenarios where numerous users compete for limited resources. Traditional methods, such as the Hungarian algorithm and greedy allocation, primarily focus on maximizing spectral efficiency, often neglecting fairness in resource distribution. This paper introduces a Shapley value-based framework that integrates Semantic Spectral Efficiency (S-SE) with fairness-driven prioritization. By leveraging Shapley values, the framework quantifies each user’s marginal contribution to overall system performance, enabling equitable and context-aware resource allocation. Semantic similarity is embedded directly into the allocation process, allowing the proposed method to intelligently manage resources based on channel conditions and the semantic relevance of transmitted information. Experimental evaluations demonstrate the framework’s effectiveness, achieving significant improvements in fairness, as measured by Jain’s Fairness Index, without compromising spectral efficiency. The Shapley-based approach outperforms established methods, including the Hungarian algorithm, reinforcement learning algorithms like Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), as well as conventional 4G and 5G resource allocation strategies. Notably, as the number of channels increases, S-SE stabilizes while fairness continues to improve, approaching optimal levels in diverse system configurations. Although the Shapley value calculation introduces a moderate increase in computational cost, this trade-off is justified by the robust balance achieved between fairness, efficiency, and overall system performance. These results highlight the potential of the proposed framework to advance next-generation wireless communication systems by prioritizing semantic relevance and equitable resource allocation.