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Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks

机译：无细胞网络中节能波束成形设计的深度增强学习

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Cell-free network is considered as a promising architecture for satisfying more demands of future wireless networks, where distributed access points coordinate with an edge cloud processor to jointly provide service to a smaller number of user equipments in a compact area. In this paper, the problem of uplink beamforming design is investigated for maximizing the long-term energy efficiency (EE) with the aid of deep reinforcement learning (DRL) in the cell-free network. Firstly, based on the minimum mean square error channel estimation and exploiting successive interference cancellation for signal detection, the expression of signal to interference plus noise ratio (SINR) is derived. Secondly, according to the formulation of SINR, we define the long-term EE, which is a function of beam-forming matrix. Thirdly, to address the dynamic beamforming design with continuous state and action space, a DRL-enabled beamforming design is proposed based on deep deterministic policy gradient (DDPG) algorithm by taking the advantage of its double-network architecture. Finally, the results of simulation indicate that the DDPG-based beamforming design is capable of converging to the optimal EE performance. Furthermore, the influence of hyper-parameters on the EE performance of the DDPG-based beamforming design is investigated, and it is demonstrated that an appropriate discount factor and hidden layers size can facilitate the EE performance.
机译：无电池网络被认为是用于满足未来无线网络的更多需求的有前途的架构，其中与边缘云处理器坐标的分布式接入点坐标，以共同为紧凑区域中的较少数量的用户设备提供服务。在本文中，研究了上行链路波束形成设计的问题，借助于无电池网络中的深增强学习（DRL）来最大化长期能量效率（EE）。首先，基于最小均方误差信道估计和利用信号检测的连续干扰消除，导出信号与干扰加噪声比（SINR）的表达。其次，根据SINR的制剂，我们定义了长期EE，这是梁形成基质的函数。第三，为了通过连续状态和动作空间来解决动态波束成形设计，通过采用双网络架构的优势，基于深度确定性政策梯度（DDPG）算法，提出了一种支持的波束成形设计。最后，仿真结果表明基于DDPG的波束形成设计能够将其融合到最佳EE性能。此外，研究了超参数对基于DDPG的波束形成设计的EE性能的影响，并证明了适当的折扣因子和隐藏层大小可以促进EE性能。

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