基于深度强化学习的密集无线接入网多基站协同功率管理
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
AI总结：
本文针对密集无线接入网能效优化问题，提出基于云RAN和深度Q网络的多基站协同功率管理方法，在用户业务量与速率约束下实现近最优能效，仿真显示较Q学习和休眠方案分别提升5%和10%能效。
摘要：
网络能效是无线通信系统设计与运行的核心支柱之一。本文研究了一种能够在基站侧进行辐射功率管理的密集无线接入网。为提升长期网络能效，本文在用户业务量和可达速率的约束下，通过协同管理多基站的辐射功率水平，构建了一个优化问题。考虑到用户侧业务到达的随机性和网络干扰的时变性，本文首先将该问题建模为马尔可夫决策过程，然后基于云无线接入网运行方案，提出了一种新的深度强化学习框架。为应对复杂度与性能之间的权衡，本文利用深度Q网络对具有乘法复杂度约束的多基站能效整体优化问题进行建模，以逼近最优性能。在深度Q网络中，每个基站首先最大化自身能效，再与其他基站协作以最大化多基站整体能效。仿真结果表明，与Q学习和休眠方案两种基准相比，所提算法收敛更快，网络能效分别提升了5%和10%。
英文摘要：
Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs radiated power levels with constraints on the users traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs energy efficiency with the multiplicative complexity constraint is modeled to achieve nearoptimal performance by using a deep Q-network (DQN). In DQN,each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multiBSs energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
