在使用深度增强学习的上联系统中对带宽和功率分配的联合优化
Chongli Zhang1, Tiejun Lv1, Pingmu Huang2
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究引入了基于联合优先级的强化学习 (JPRL) 方法,以优化无线资源分配,显著提高系统吞吐量,减少多用户系统中的干扰.
科学领域:
- 无线通信系统无线通信系统
- 电信领域的人工智能
- 网络中的资源管理.
背景情况:
- 多细胞系统中越来越多的用户会导致爆炸性干扰,降低通信质量.
- 细胞间干扰是优化无线资源利用的一个主要挑战.
- 现有的方法努力平衡吞吐量最大化与服务质量限制.
研究的目的:
- 为联合优化带宽和发射功率分配提出一种新的方法.
- 为了提高系统吞吐量,同时抑制共同通道干扰.
- 为了保证无线网络中的服务质量 (QoS) 约束.
主要方法:
- 开发了一种基于共同优先级的强化学习 (JPRL) 方法.
- 将联合问题解为带宽分配和功率分配子问题.
- 使用多代理双深Q网络 (MADDQN) 进行带宽分配,并对权力分配进行优先级的多代理深确定性政策梯度 (P-MADDPG).
主要成果:
- 通过JPRL方法,可以证明加速模型训练.
- 与替代方法相比,实现了优越的系统吞吐量.
- 平均吞吐量比同质学习基准高10.4-15.5%,比遗传算法高17.3%.
结论:
- 拟议的JPRL方法有效地优化了无线资源利用率.
- JPRL显著提高了系统吞吐量,并减轻了干扰.
- 这种方法为未来的无线通信系统提供了一个有希望的解决方案.
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