Optimization of Energy Consumption in Semantic Communication Networks for Image Recovery Tasks

ObjectiveWith the rapid development of semantic communication and the increasing demand for high-fidelity image recovery, high computational and transmission energy consumption remains a key factor limiting network deployment. Existing resource management strategies are largely static and show limited adaptability to dynamic wireless environments and user mobility. To address these issues, a robust energy optimization strategy driven by a modified Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is proposed. By jointly optimizing communication and computing resources, the total network energy consumption is minimized while strictly satisfying multi-dimensional constraints, including latency and image recovery quality.MethodsFirst, a theoretical model of the semantic communication network is constructed, and a closed-form expression for the user Symbol Error Rate (SER) is derived through asymptotic analysis of the uplink Signal-to-Interference-plus-Noise Ratio (SINR). Subsequently, the coupling relationships among semantic extraction rate, transmit power, computing resources, and network energy consumption are quantified. On this basis, a joint optimization model is formulated to minimize total energy consumption under constraints of delay, accuracy, and reliability. To solve this mixed-integer nonlinear programming problem, a modified MAPPO algorithm is designed. The algorithm integrates Long Short-Term Memory (LSTM) networks to capture temporal dynamics of user positions and channel states, and introduces a noise mechanism into the global state and advantage function to improve policy exploration and robustness.Results and DiscussionsSimulation results show that the proposed algorithm consistently outperforms baseline methods, including standard MAPPO, NOISE-MAPPO, LSTM-MAPPO, MADDPG, and greedy algorithms. The proposed strategy accelerates training convergence by 66.7%～80% relative to the benchmarks. In dynamic environments, network energy consumption stability is improved by approximately 50%, and user latency stability is enhanced by more than 96%. Additionally, the average SER is reduced by 4%～16.33% without degrading final image recovery performance, demonstrating an effective balance between energy efficiency and task reliability.ConclusionsThis study addresses energy optimization in semantic communication networks by combining theoretical modeling with a modified deep reinforcement learning framework. The proposed decision-making approach enhances the standard MAPPO algorithm through LSTM-based temporal feature extraction and noise-assisted robust exploration. Simulation results in dynamic single-cell and multi-cell scenarios show that the method improves convergence efficiency and system stability, and achieves a favorable trade-off between energy consumption and service quality. These results provide a theoretical basis and an efficient resource management framework for future energy-constrained semantic communication systems.