To accommodate the explosive wireless traffics, massive multiple-input multiple-output (MIMO) is regarded as one of the key enabling technologies for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance the network performance. In this paper, we investigate the sum rate maximization problem in a massive MIMO mobile cellular network, where in each cell a multi-antenna BS serves multiple mobile users simultaneously via downlink beamforming. Although existing optimization-based CBF algorithms can provide near-optimal solutions, they require realtime and global channel state information (CSI), in addition to their high computation complexity. It is almost impossible to apply them in practical wireless networks, especially highly dynamic mobile cellular networks. Motivated by this, we propose a deep reinforcement learning based distributed dynamic coordinated beamforming (DDCBF) framework, which enables each BS to determine the beamformers with only local CSI and some historical information from other BSs.Besides, the beamformers can be calculated with a considerably lower computational complexity by exploiting neural networks and expert knowledge, i.e., a solution structure observed from the iterative procedure of the weighted minimum mean square error (WMMSE) algorithm. Moreover, we provide extensive numerical simulations to validate the effectiveness of the proposed DRL-based approach. With lower computational complexity and less required information, the results show that the proposed approach can achieve comparable performance to the centralized iterative optimization algorithms.
翻译:为适应爆炸性的无线通信流量,巨型多输入多输出(MIMO)被视为下一代通信系统的关键技术之一。在巨型MIMO蜂窝网络中,协调波束赋形(CBF)是一种增强网络性能的有效方法,它联合设计多个基站(BS)的波束赋形器。在每个小区中,多天线BS通过下行波束赋形同时为多个移动用户提供服务,并研究了巨型MIMO移动蜂窝网络中的总和速率最大化问题。虽然现有的基于优化的CBF算法可以提供接近最优解,但它们需要实时和全局的信道状态信息(CSI),并且计算复杂度较高。在实际无线网络中,特别是高度动态的移动蜂窝网络中几乎不可能应用它们。鉴于此,本文提出了一种基于深度强化学习的分布式动态协调波束赋形(DDCBF)框架,它使每个BS能够仅使用本地CSI和其他BS的某些历史信息来确定波束赋形器。此外,通过利用神经网络和专家知识,即加权最小均方误差(WMMSE)算法迭代过程中观察到的解决方案结构,可以计算出具有相当较低的计算复杂度的波束赋形器。此外,我们提供了大量的数值模拟来验证所提出的基于DRL的方法的有效性。结果表明,该方法可实现与集中式迭代优化算法相当的性能,计算复杂度更低,所需的信息更少。