  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.
