通过基于混合启发式的深度学习,为D2D通信进行节能联合中继选择和资源分配,利用基于混合启发性的深度学习
C H Ramesh Babu1, S Nandakumar2
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Scientific reports
|July 11, 2025
概括
本研究介绍了5G设备对设备 (D2D) 网络的新型优化方法,增强继电器选择和资源分配,以提高频谱和能源效率. 该方法解决了现代无线通信中的干扰和可持续性挑战.
科学领域:
- 无线通信网络 无线通信网络
- 优化算法 优化算法
- 机器学习应用 机器学习应用
背景情况:
- 第五代 (5G) 网络旨在提高设备对设备 (D2D) 通信等创新的数据速率.
- 继电器辅助的D2D通信面临挑战,包括继电器的干扰和能源消耗,降低系统性能.
- 现有的方法在复杂的网络环境中难以高效的中继选择和资源分配.
研究的目的:
- 为5G D2D通信提出一个高效的中继选择和资源分配策略.
- 通过解决相互干扰和能源可持续性问题来提高系统性能.
- 为了提高网络效率,利用先进的优化和机器学习.
主要方法:
- 开发了一种基于厨师的优化 (HMRFCO) 算法的新型混合鱼捕食.
- 继电器的选择标准包括频谱效率,能源效率,吞吐量,延迟和网络容量.
- 通过HMRFCO优化的一种自适应性残留封闭循环单元 (AResGRU) 模型被用于中继号码预测和资源分配.
主要成果:
- HMRFCO算法有效地优化了AResGRU模型用于预测任务.
- 提出的方法实现了考虑多个性能指标的高效继电器选择.
- 该AResGRU模型成功预测了最佳的继电器数量和资源分配.
结论:
- 开发的HMRFCO和AResGRU综合方法为5G D2D网络中中继选择和资源分配提供了有效的解决方案.
- 这种方法可以提高光谱和能源效率,同时减轻干扰.
- 该研究强调了混合优化和深度学习对可持续和高性能无线网络的潜力.
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