DRL-based Resource Management for Task-Centered Semantic Communication

The evolution of Artificial Intelligence (AI) integrated with the Sixth-generation ($\mathbf{6 G}$) framework poses significant challenges to low-latency applications. Recently, semantic communication has emerged as a promising technique for future intelligent applications. However, the resource management problem combined with semantics is not fully explored. In this paper, we present a deep reinforcement learning-based twin-delayed deep deterministic policy gradient (TD3) for task-centered semantic communication. The proposed TD3 algorithm optimizes bandwidth, and semantic information and prioritizes data with maximum signal-to-noise ratio (SNR) for the efficient transmission of useful information. Simulation results demonstrate the effectiveness of the proposed TD3 scheme compared to state-of-the-art work in terms of transmission efficiency by up to $36 \%$ for varying users and up to $33 \%$ for varying SNR.