The emergence of the semantic-aware paradigm presents opportunities for innovative services, especially in the context of 6G-based applications. Although significant progress has been made in semantic extraction techniques, the incorporation of semantic information into resource allocation decision-making is still in its early stages, lacking consideration of the requirements and characteristics of future systems. In response, this paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the $\alpha$-fairness metric, while accounting for user data correlation by introducing the concepts of self- and assisted throughputs. Initially, the problem is analyzed to identify its optimal solution. Subsequently, a Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed. This method is grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), enabling the user equipment to autonomously make decisions regarding wireless spectrum access based solely on their local individual observations. The efficiency of the proposed technique is evaluated through two scenarios: single-channel and multi-channel. The findings illustrate that, across a spectrum of $\alpha$ values, association matrices, and channels, SAMA-D3QL consistently outperforms alternative approaches. This establishes it as a promising candidate for facilitating the realization of future federated, dynamically evolving applications.

翻译：语义感知范式的兴起为创新服务带来了机遇，尤其在6G应用背景下。尽管语义提取技术已取得显著进展，但将语义信息纳入资源分配决策仍处于早期阶段，缺乏对未来系统需求与特性的考量。为此，本文针对无线频谱多址接入问题提出了一种新颖的建模方法。该方法旨在优化利用效率与公平性的权衡（采用α-公平性度量），同时通过引入自身吞吐量与辅助吞吐量的概念来考虑用户数据相关性。首先，通过对问题的分析确定其最优解。随后，提出了一种基于无模型多智能体深度强化学习的语义感知多智能体双重竞争深度Q学习技术。该方法使用户设备能够仅依据本地个体观测，自主决策无线频谱接入。通过单信道与多信道两种场景评估了所提技术的效能。结果表明，在多种α取值、关联矩阵及信道配置下，SAMA-D3QL均持续优于其他方法，这使其成为推动未来联邦式动态演进应用实现的有力候选方案。