使用元启发算法对认知无线电网络进行集群合作频谱传感的性能提升
Vikas Srivastava1,2, Parulpreet Singh3, Shubham Mahajan4,5,6
1Lovely Professional University, Phagwara, India.
Scientific reports
|October 6, 2023
概括
这项研究优化了认知无线电网络 (CRN),使用混合机器学习方法进行合作频谱传感 (CSS). 新方法提高了频谱缺口检测,提高了效率,减少了动态频谱访问中的错误.
科学领域:
- 无线通信是一种无线通信.
- 认知无线电网络是一种认知无线电网络.
- 信号处理 信号处理
背景情况:
- 认知无线电网络 (CRN) 旨在通过使二级用户 (SU) 能够动态检测和访问许可频谱来提高频谱利用率.
- 合作频谱传感 (CSS) 提高了检测准确性,但在数据聚合和能源消耗方面面临挑战.
- 集群技术将SU组合起来,以简化向聚变中心 (FC) 的数据传输,从而有可能提高效率.
研究的目的:
- 通过基于集群的高级CSS技术优化CRN的检测性能.
- 引入一种新的混合机器学习算法,以有效识别频谱差距.
- 为解决CSS常规集群方法中普遍存在的计算复杂性问题.
主要方法:
- 开发了一种混合支持矢量机 (SVM) 和红鹿算法 (RDA) 的混合SVM-RDA.
- 该算法应用于CRN.中的基于集群的合作频谱传感框架.
- 性能根据检测概率 (Pd) 和错误概率 (Pe) 进行评估.
主要成果:
- 混合SVM-RDA算法与传统集群技术相比,表现出更高的性能.
- 实现了高检测概率 (高达99%) 和低错误概率 (高达1%).
- 在计算复杂性方面表现优于现有方法.
结论:
- 拟议的混合SVM-RDA算法有效地优化CRN检测性能使用集群CSS.
- 这种方法为动态频谱访问提供了显著的效率和准确性的改进.
- 该方法为提高认知无线电网络的可靠性提供了一个有希望的解决方案.
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