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基于深度强化学习的频谱效率高的用户分组和资源分配,用于毫米波巨型MIMO-NOMA系统

Minghao Wang¹, Xin Liu¹, Fang Wang¹

¹College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China.

Scientific reports

|April 17, 2024

概括

本研究介绍了毫米波巨型MIMO-NOMA系统的深度强化学习框架. 它优化了资源分配,提高了6G网络的频谱效率和系统容量.

科学领域:

无线通信网络是无线通信网络.
信号处理信号处理
机器学习应用程序机器学习应用程序

背景情况:

毫米波 (mmWave) 大规模的多输入多输出非对角多访问 (MIMO-NOMA) 对6G至关重要.
毫米波大规模MIMO-NOMA系统中用户和天线的增加在干扰抑制和资源分配方面带来了挑战.

研究的目的:

提出一个频谱高效和快速融合的深度强化学习 (DRL) 框架.
在毫米波大规模MIMO-NOMA系统中优化用户分组,子通道分配和功率分配.

主要方法:

一个增强的K-means算法用于用户分组,以减少干扰和加速融合.
一个对决的深度Q网络 (DQN) 用于分频道分配,提高了合速度.
一种基于深度决定性政策梯度 (DDPG) 的算法,用于分配电力资源,以提高系统总和率.

主要成果:

与其他基于神经网络的算法相比,拟议的DRL框架显示出优越的融合性能.
该方案实现了比贪,随机,RNN和DoubleDQN算法更高的系统容量.
基于DDPG的功率分配可以避免功率量化造成的性能损失.

结论:

开发的DRL框架有效地解决了毫米波大规模MIMO-NOMA系统中的资源分配挑战.
提出的方法显著提高了融合速度和系统容量.
这种方法为高效的6G无线通信提供了一个有希望的解决方案.

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