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

CHEN Yang; MA Huan; JI Zhi; LI Ying Qi; LIANG Jia Yu; GUO Lan

doi:10.11999/JEIT250915

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CHEN Yang, MA Huan, JI Zhi, LI Ying Qi, LIANG Jia Yu, GUO Lan. Optimization of Energy Consumption in Semantic Communication Networks for Image Recovery Tasks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250915

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CHEN Yang, MA Huan, JI Zhi, LI Ying Qi, LIANG Jia Yu, GUO Lan. Optimization of Energy Consumption in Semantic Communication Networks for Image Recovery Tasks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250915

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

doi: 10.11999/JEIT250915 cstr: 32379.14.JEIT250915

1.
Army Engineering University of PLA Nanjing 210007 China
2.
Tong Fang Electronic Science & Technology Co., Ltd Jiujiang 332005 China

Accepted Date: 2025-12-08
Rev Recd Date: 2025-12-08

Available Online: 2025-12-13

Abstract

Abstract

Objective With the rapid development of semantic communication and the increasing demand for high-fidelity image recovery, high computation and transmission energy consumption have become critical issues limiting network deployment. However, existing resource management strategies are mostly static and have limitations in adapting to dynamic wireless environments and user mobility. To address these challenges, a robust energy optimization strategy driven by a modified Multi-Agent Proximal Policy Optimization (MAPPO) algorithm has emerged as a promising approach. By jointly optimizing communication and computing resources, it is possible to minimize the total network energy consumption while strictly satisfying multi-dimensional constraints such as latency and image recovery quality. Methods First, a theoretical model for the semantic communication network is constructed , and a closed-form expression for the user Symbol Error Rate (SER) is derived via asymptotic analysis of the uplink Signal-to-Interference-plus-Noise Ratio (SINR). Subsequently, the coupling relationship among semantic extraction rate, transmit power, computing resources, and network energy consumption is quantified. Based on this, a joint optimization model is established to minimize the total energy under constraints of delay, accuracy, and reliability. To solve this complex mixed-integer nonlinear programming problem, a modified MAPPO algorithm is designed. This algorithm incorporates 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 enhance policy exploration and robustness. Results and Discussions Simulation results demonstrate that the proposed algorithm significantly outperforms baseline methods (including standard MAPPO, NOISE-MAPPO, LSTM-MAPPO, MADDPG, and Greedy algorithms). Specifically, the proposed strategy accelerates the training convergence speed by 66.7%–80% compared to benchmarks. Furthermore, the algorithm exhibits superior stability in dynamic environments, improving the stability of network energy consumption by approximately 50% and user latency stability by over 96%. Additionally, the average SER is effectively reduced by 4%–16.33% without compromising the ultimate image recovery performance, verifying the algorithm's capability to balance energy efficiency and task reliability. Conclusions This paper addresses the challenge of energy optimization in semantic communication networks by integrating theoretical modeling with a modified deep reinforcement learning framework. The proposed decision-making method enhances the standard MAPPO algorithm by leveraging LSTM for temporal feature extraction and noise mechanisms for robust exploration. The method is evaluated through simulations in dynamic single-cell and multi-cell scenarios, and the results show that: (1) The proposed method significantly improves convergence efficiency and system stability over the baselines; (2) A better trade-off between energy consumption and service quality is achieved, providing a theoretical foundation and an efficient resource management framework for future energy-constrained semantic communication systems.
- Semantic communication,
- Resource management,
- Energy consumption,
- Multi-agent deep reinforcement learning

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References(22)

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