面向图像恢复任务的语义通信网络能耗优化

陈阳; 马欢; 姬智; 李英奇; 梁佳宇; 郭兰

doi:10.11999/JEIT250915

面向图像恢复任务的语义通信网络能耗优化

doi: 10.11999/JEIT250915 cstr: 32379.14.JEIT250915

陈阳^1, ,,
马欢²,
姬智¹,
李英奇¹,
梁佳宇¹,
郭兰¹

1.
中国人民解放军陆军工程大学南京 210007
2.
同方电子科技有限公司九江 332005

基金项目: 国家优秀青年科学基金(62122094)，江苏省基础研究计划重点项目(BK20253031)

详细信息

作者简介:
陈阳：男，博士生，研究方向为面向任务的多模态语义通信资源管理

马欢：男，高级工程师，研究方向为通信信号分析与处理

姬智：男，博士生，研究方向为D2D内容分享和语义通信

李英奇：男，博士生，研究方向为多模态语义通信

梁佳宇：男，博士生，研究方向为多模态语义通信

郭兰：女，博士生，研究方向为智能信号处理

通讯作者:
陈阳　chenyangbabm@126.com

中图分类号: TP393.1
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-15
- 修回日期: 2025-12-08
- 录用日期: 2025-12-08
- 网络出版日期: 2025-12-13

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

CHEN Yang^{1
, ,},
MA Huan²,
JI Zhi¹,
LI Yingqi¹,
LIANG Jiayu¹,
GUO Lan¹

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

Funds: The National Science Fund of China for the Excellent Young Scholars (62122094), The Key Projects of the Basic Research Program of Jiangsu Province (BK20253031)

摘要

摘要: 针对语义通信网络在图像恢复任务中计算和传输能耗过高的问题，该文提出一种改进型多智能体近端策略优化算法驱动的网络能耗优化策略，在保障任务性能的同时最小化网络总能耗。首先，量化分析语义提取率、发射功率、计算资源与网络能耗间的耦合关系。随后，构建以小区总能耗最小化为目标，同时满足时延、图像恢复质量等多维约束的优化模型。最后，设计改进型多智能体近端策略优化算法对该模型进行求解。仿真结果表明，与基准算法相比，所提算法在维持相当能耗水平的同时，训练收敛速度提升66.7%～80%，网络能耗和用户时延稳定性显著提升，并能有效降低平均误符号率。
- 语义通信 /
- 资源管理 /
- 能耗 /
- 多智能体深度强化学习
Abstract: Objective With 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. Methods First, 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 Discussions Simulation 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. Conclusions This 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.
- Semantic communication /
- Resource management /
- Energy consumption /
- Multi-agent deep reinforcement learning

HTML全文

图 1 单小区语义通信网络示意图

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图 2 用户与基站通过上行链路进行图像传输与恢复过程示意图

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图 3 不同算法下网络链路速率性能示意图

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图 4 多小区场景不同算法网络性能示意图

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表 1 仿真参数

仿真参数	参数值
小区数量	1
基站天线数量N_r	32
小区用户数量M	25
用户天线数量	1
用户最大发射功率P_k(mW)	100
功率$ \left\{{p_{l1},p_{l2},p_{l3},p_{l4}}\right\} $(mW)	{25, 50, 75, 100}
语义提取率$ \{{\rho }_{{\mathrm{l1}}},{\rho }_{{\mathrm{l2}}} $, ${\rho }_{{\mathrm{l3}}},{\rho }_{{\mathrm{l4}}},{\rho }_{{\mathrm{l5}}},{\rho }_{{\mathrm{l6}}}\} $	{1/6,2/6,3/6,4/6,5/6,1}
小区半径(m)	900
用户上行信道带宽W(MHz)	1
能耗系数$ \kappa $	10^–28
时延约束$ t\mathrm{_{th}} $(ms)	100
用户计算容量f (GHz)	2
ECIW计算容量$ {F}_{\max } $(GHz)	25
噪声功率(dBm/Hz)	–174

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表 2 所提算法与基准算法的网络误符号性能对比

算法	所有用户每回合最大平均误符号率均值	所有用户每回合最大平均误符号率方差
所提算法	$ 1.3796\times {10}^{-4} $	$ 6.5535\times {10}^{-7} $
MAPPO	$ 1.4420\times {10}^{-4} $	$ 6.8344\times {10}^{-7} $
LSTM-MAPPO	$ \text{1.5349}\times {\text{10}}^{{-4}} $	$ 7.8464\times {10}^{-7} $
NOISE-MAPPO	$ \text{1.6489}\times {\text{10}}^{{-4}} $	$ 7.2612\times {10}^{-7} $
MADDPG	$ \text{1.5251}\times {\text{10}}^{{-4}} $	$ 7.1457\times {10}^{-7} $
贪婪算法	$ \text{8.9753}\times {\text{10}}^{{-3}} $	$ 6.5866\times {10}^{-6} $

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表 4 所提算法与基准算法的图像恢复性能对比(Imagenet数据集)

算法	所有用户每回合平均峰值信噪比均值(dB)	所有用户每回合平均峰值信噪比方差
所提算法	30.1345	0.0017
MAPPO	30.1005	0.0024
LSTM-MAPPO	29.5246	0.0084
NOISE-MAPPO	29.3471	0.0101
MADDPG	28.8562	0.0075
贪婪算法	11.1175	0.0159

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表 3 所提算法与基准算法的图像恢复性能对比(MNIST数据集)

算法	所有用户每回合平均峰值信噪比均值(dB)	所有用户每回合平均峰值信噪比方差
所提算法	35.1475	0.0026
MAPPO	35.1005	0.0054
LSTM-MAPPO	34.5487	0.0097
NOISE-MAPPO	34.2211	0.0097
MADDPG	33.9256	0.0065
贪婪算法	15.6624	0.0219

下载: 导出CSV

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