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探索5G上链通信的潜力:联合电源控制,用户分组和多学习灰狼优化器的协同集成

Sobana Sikkanan¹, Chandrasekaran Kumar², Premkumar Manoharan³

¹Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India. sobanaa@gmail.com.

Scientific reports

|September 13, 2024

概括

这项研究优化了非直角多重接入 (NOMA) 频谱效率,使用了针对5G/6G网络的增强的灰狼优化器 (GWO). 改进的GWO算法有效地管理功率控制和用户分组,优于复杂性降低的标准方法.

科学领域:

无线通信网络是无线通信网络.
信号处理和优化的信号处理和优化.

背景情况:

非直角多重接入 (NOMA) 为5G/6G网络提供了增强的频谱效率.
优化NOMA性能需要联合电源控制和用户分组,这可能是计算密集的.
现有的近最佳解决方案往往涉及大量的系统和计算开销.

研究的目的:

调查联合功率控制和用户分组,以优化NOMA上链系统的光谱效率.
为了减少与NOMA系统优化相关的计算难度.
为了提高NOMA应用程序的元启发优化算法的性能.

主要方法:

开发了一个改进的灰狼优化器 (GWO),集成竞争性学习,Q学习和贪选择.
竞争性学习平衡了勘探/开发,并保护了多样性.
Q学习提高了适应性,防止了冗余的搜索;贪的选择保留了最佳的解决方案.

主要成果:

提议的增强GWO算法有效地管理了NOMA系统中的功率控制和用户分组.
模拟结果显示,这种方法在光谱效率方面超过了传统的直角多重接入.
该算法在标准GWO和其他最先进的方法中表现出卓越的性能,系统复杂性降低.

结论:

集成增强的GWO算法显著提高了NOMA上链系统的性能.
与现有技术相比,这种方法实现了更高的光谱效率和更低的计算复杂性.
该研究为NOMA系统优化提供了有效和计算效率高的解决方案.

关键词:

竞争性学习竞争性学习灰狼优化器灰狼优化器非对角的多重访问 (NOMA)这就是Q-learning.频谱效率是指光谱的使用效率.用户分组使用户分组.

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