Energy-Efficient Resource Allocation for Multi-User Semantic Communications: A Deep Reinforcement Learning Approach

Effective resource allocation is critical to the efficiency of semantic communications, especially when faced with limited energy supplies and constrained communication resources. The unique requirements of semantic communications have brought new challenges to the joint optimization of energy and communication resources. This paper presents a novel deep reinforcement learning (DRL)-based algorithm to maximize the long-term average semantic energy efficiency (S-EE) for energy harvesting-powered semantic communication systems. Particularly, the Multi-discrete Proximal Policy Optimization (MPPO) is applied to determine discrete actions, including channel allocation and the number of semantic symbols, while the Deep Deterministic Policy Gradient (DDPG) is employed to learn continuous transmit powers. MPPO uses multiple heads to cope with multi-discrete actions, thereby reducing the output dimension of actions. Simulation results indicate that the proposed algorithm achieves faster convergence and enhances S-EE by 31.6%, compared to its benchmarks.