连接地图诱导的资源分配方案用于认知无线电网络服务质量最大化
K Saravanan1, R Jaikumar2, Stalin Allwin Devaraj3
1Department of Mechatronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, Tamilnadu, India.
Scientific reports
|April 24, 2025
概括
认知无线电网络通过公平的资源分配实现服务质量 (QoS). 一个新的联网资源地图诱导的资源分配计划 (CRM-RAS) 提高了二级用户的公平性和资源利用率.
科学领域:
- 无线通信网络是无线通信网络.
- 电信的资源管理 电信中的资源管理
- 认知无线电网络是一种认知无线电网络.
背景情况:
- 认知无线电网络 (CRN) 的服务质量 (QoS) 依赖于对二级用户进行公平的资源分配和调度.
- 实现公平性需要定期更新多用户分配的公平性指数,无论道容量如何.
研究的目的:
- 为认知无线电网络引入一种新的联网资源地图诱导的资源分配方案 (CRM-RAS).
- 为了提高二级用户的公平性和资源利用,同时满足QoS需求.
- 通过智能调度和分布式学习,提高资源分配的效率.
主要方法:
- 该CRM-RAS识别无线电和用户资源的可用性,从主要用户构建一个分配地图.
- 公平指数是使用最大资源利用率和QoS因子进行更新的,该 QoS因子是基于低延迟和高分配率计算的.
- 主要和次要用户之间的分布式联合学习验证了公平指数,如果它低于分配率,则会触发并发调度.
主要成果:
- 拟议的CRM-RAS方案显示了网络性能的显著改善.
- 与现有方法相比,实现了比现有方法高8.15%的总和率.
- 在最大信号对噪声比率 (SNR) 时,错误率降低了8.88%.
结论:
- 在CRN中,CRM-RAS有效地提高了公平性和资源利用率.
- 分布式联合学习辅助器通过并发调度来维护和改进公平指数.
- 该方案确保了高效和公平的资源分配,从而提高网络性能并满足QoS需求.
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