在邻近区域网络中为智能电网通信提供高效的次载波分配
Mohammad Ikram1, Pan Zhiwen1,2, Liu Nan1
1National Mobile Communications Research Laboratory, Southeast University, Nanjing, China.
PloS one
|November 7, 2025
概括
适应性层次多目标资源优化器 (AH-MORO) 框架通过优化子载波分配以获得更好的吞吐量,更低的延迟和在动态环境中减少能源消耗来增强智能电网邻里区域网络.
科学领域:
- 无线通信网络是无线通信网络.
- 智能电网技术是智能电网的技术.
- 资源优化 资源优化
背景情况:
- 智能电网邻近区域网络 (NAN) 的现有方法与动态,高密度环境作斗争,导致静态资源配置,干扰管理不佳和可扩展性问题.
- 这些局限性导致网络性能低于最佳,包括吞吐量降低,延迟更高,能源消耗增加.
研究的目的:
- 引入适应性层次多目标资源优化器 (AH-MORO) 框架,用于在智能电网NAN中进行高级子载波分配.
- 解决当前资源分配策略在动态和高密度网络条件下的关键局限性.
- 为实时,节能,耐干扰的智能电网通信开发一个整体解决方案.
主要方法:
- 实施了具有适应权重 (λ1, λ2, λ3) 的层次多目标优化模型,以平衡吞吐量,延迟和能源效率.
- 开发了一种双层干扰缓解系统,包括基于约束的子载波分配和自适应功率控制.
- 利用混合基因算法-深度增强学习元启发式解决方案,在波动的流量负载下实现实时,低复杂度的优化.
主要成果:
- 艾哈·莫罗 (AH-MORO) 对最先进的方法进行了显著改进,实现了37.5%更高的吞吐量,34.2%更低的延迟,并减少了24%的能源消耗.
- 该框架在密集的城市NAN (1,000多个设备) 中实现了33.3%的干扰减少改进.
- 通过公平约束来保证服务质量 (QoS),确保所有用户的最低吞吐量,延迟和能量限制.
结论:
- AH-MORO被认可为下一代智能电网NAN适应性资源管理的第一个整体解决方案.
- 该框架为实时,节能和抗干扰的通信系统提供了一个新的基准.
- 在智能电网通信中,AH-MORO有效地应对动态和高密度环境的挑战.
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