在IRS协助的认知无线电网络资源配置研究
Shuo Shang1, Zhiyong Chen1, Dejian Zhang1
1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究提高了认知无线电网络的能源效率,使用智能反射表面 (IRS) 辅助模型. 它最大限度地提高了二级用户的效率,同时保护了主要用户,提高了系统的整体性能.
科学领域:
- 无线通信网络是无线通信网络.
- 资源配置优化资源分配优化
- 信号处理 信号处理
背景情况:
- 长途传输中严重的信号减弱会降低认知无线电网络中的能源效率.
- 底层模式的认知无线电网络在平衡二级用户性能与主要用户干扰约束方面面临着挑战.
研究的目的:
- 开发一个智能反射表面 (IRS) 辅助的,能量受限的继电器认知无线电资源分配模型.
- 为了最大限度地提高二级用户的能源效率,同时确保满足主要用户干扰约束.
- 提高认知无线电网络的整体系统性能和通信质量.
主要方法:
- 构建一个IRS辅助和能源受限制的继电器认知无线电资源分配模型.
- 引入可控制的反射路径,以提高链路质量和能源利用率.
- 开发和应用一个混乱的蜘蛛黄蜂优化算法来解决复杂的优化问题.
- 纳入Jain公平指数,以便在二级用户之间公平地分配电力.
主要成果:
- 拟议的模型显著提高了系统的能源效率.
- 优化方法提高了通信质量的稳定性.
- 混沌蜘蛛黄蜂优化算法有效地解决了问题的非凸和高维性质,避免过早的融合.
结论:
- 美国国税局协助的资源分配模型有效地减轻了信号衰减,并提高了认知无线电网络的能源效率.
- 拟议的优化算法为复杂的资源分配问题提供了强大的解决方案,确保公平性和性能.
- 这项研究在能源限制下优化认知无线电网络性能方面取得了重大进展.
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