在NOMA-MEC网络中以低延迟为导向的联合数据压缩和资源分配:深度强化学习方法
Fangqing Tan1, Yu Zeng1, Chao Lan1
1Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了移动边缘计算 (MEC) 网络的新资源分配方法,集成数据压缩和非直角多重访问. 该方法通过优化资源和任务卸载来最大限度地减少任务处理延迟.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 移动边缘计算 (MEC) 面临着通信压力和资源限制.
- 终端设备的电池容量和计算能力有限.
- 动态道条件和任务要求带来了挑战.
研究的目的:
- 为MEC系统提出一种资源配置优化方法.
- 在实际约束下,尽量减少总任务处理延迟.
- 整合数据压缩和非对角多重访问 (NOMA) 技术.
主要方法:
- 共同优化计算资源分配,任务卸载和数据压缩比率.
- 制定一个优化模型,以尽量减少总任务处理延迟.
- 为动态环境开发软max深度双确定性政策梯度 (D3PG) 算法.
主要成果:
- 拟议的softmax D3PG算法有效地优化了资源分配,任务卸载和压缩比.
- 该方法成功地将总任务处理延迟最小化,同时遵守约束.
- 与基准算法相比,模拟结果显示出更高的性能.
结论:
- 综合方法显著降低了MEC的通信压力和资源限制.
- 拟议的算法提高了融合速度和任务处理效率.
- 这种方法为优化有限资源的MEC系统提供了可行的解决方案.
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