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深度决定性政策基于梯度的资源分配,考虑网络切片和移动网络中的设备对设备通信
Hudson Henrique de Souza Lopes1, Lucas Jose Ferreira Lima1, Telma Woerle de Lima Soares2
1Electrical, Mechanical and Computer (EMC) School of Engineering, Federal University of Goias (UFG), Goiânia 74605010, GO, Brazil.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了DDPG-KRP,这是一种用于下一代移动网络动态资源配置的深度强化学习方法. 它有效地管理网络切片和设备对设备通信,优于其他算法.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 下一代移动网络 (B5G/6G) 需要多样化的资源管理.
- 网络切片 (NS) 和设备对设备 (D2D) 通信是提高效率的关键技术.
- 整合NS和D2D可以提高频谱利用率和网络性能.
研究的目的:
- 用NS和D2D解决无线网络中的动态资源分配挑战.
- 为这个复杂的问题开发一种高效的深度强化学习 (DRL) 方法.
- 优化资源分配政策,以最大限度地提高长期网络回报.
主要方法:
- 拟议的DDPG-KRP:一种DRL方法,结合了深度决定性政策梯度 (DDPG),K-最近邻居 (KNNs) 和奖励惩罚 (RP).
- DDPG-KRP通过最大化累积奖励来确定资源分配政策.
- RP用于消除不必要的行为,完善DRL代理人的决策过程.
主要成果:
- DDPG-KRP在使用切片的无线网络的动态资源分配方面表现出了效率.
- 拟议的方法在模拟实验中表现优于其他DRL算法.
- 实现了NS和D2D通信组合资源的有效管理.
结论:
- DDPG-KRP为先进移动网络的资源配置提供了一个强大的解决方案.
- 整合DRL,NS和D2D通信为未来的网络优化提供了一个有希望的方向.
- 这种方法提高了在各种网络环境中的可扩展性和性能.
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