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基于强化学习的多无线电系统的强有力的资源管理.
James Delaney1, Steve Dowey1, Chi-Tsun Cheng1
1Manufacturing, Materials and Mechatronics, School of Engineering, STEM College, RMIT University, 124 La Trobe St., Melbourne, VIC 3000, Australia.
使用多目标增强学习的智能无线电选择提高了物联网 (IoT) 的无线通信稳定性. 与传统方法相比,自适应式勘探策略可以提高20%的性能.
科学领域:
- 无线通信系统无线通信系统
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 适应性无线电技术适应性无线电技术
背景情况:
- 在物联网中,对具有多个无线收发器的传感器设备的需求增加.
- 需要适应性系统,以确保在动态通道条件下可靠的通信.
- 专注于部署人员设备和接入点基础设施之间的无线连接.
研究的目的:
- 将多目标强化学习 (MORL) 框架应用于多无线电选择和功率控制.
- 使用独立的奖励函数来管理功耗和比特率之间的权衡.
- 开发和评估一个适应性探索战略,以实现强大的政策学习.
主要方法:
- 利用多个无线电平台,使用多种收发器技术.
- 实施了MORL框架,用于相互冲突的目标,具有独立的奖励功能.
- 建议扩展多目标SARSA算法,并采用适应式勘探策略.
主要成果:
- 通过收发器的自适应控制实现了强大可靠的链接.
- 证明有效地管理电力消耗和比特率权衡.
- 与腐烂的勘探政策相比,适应性勘探的F1得分增加了20%.
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结论:
- MORL框架有效地解决了多无线电选择和功率控制方面的挑战.
- 适应式勘探策略可以提高无线通信系统的稳定性和性能.
- 拟议的扩展多目标SARSA算法在通信可靠性方面提供了显著的改进.
