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Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO With Lens Arrays
IEEE Journal on Selected Areas in Communications ( IF 16.4 ) Pub Date : 2021-06-07 , DOI: 10.1109/jsac.2021.3087233
Qiyu Hu , Yanzhen Liu , Yunlong Cai , Guanding Yu , Zhi Ding

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

中文翻译:

联合深度强化学习和展开:使用透镜阵列的毫米波多用户 MIMO 的波束选择和预编码

具有离散透镜阵列 (DLA) 的毫米波 (mmWave) 多用户多输入多输出 (MU-MIMO) 系统因其简单的硬件实现和卓越的性能而受到极大关注。在这项工作中,我们研究了具有 DLA 的毫米波 MU-MIMO 系统的波束选择和数字预编码矩阵的联合设计,以在发射功率约束和选择矩阵结构的约束下最大化总速率。所研究的具有离散变量和耦合约束的非凸问题很难解决,提出了一种有效的联合神经网络 (NN) 设计框架来解决它。具体来说,所提出的框架由一个基于深度强化学习(DRL)的神经网络和一个深度展开的神经网络组成,分别用于优化波束选择和数字预编码矩阵。对于基于 DRL 的神经网络,我们将波束选择问题表述为马尔可夫决策过程,并开发了双深度 Q 网络算法来解决它。基站被认为是一个代理,其中的状态、动作和奖励函数都经过精心设计。关于数字预编码矩阵的设计,我们开发了一种迭代加权最小均方误差算法诱导深度展开神经网络,该算法将该算法展开为具有引入可训练参数的分层结构。仿真结果验证了这种联合训练的 NN 显着优于现有的迭代算法,具有降低的复杂性和更强的鲁棒性。我们将光束选择问题表述为马尔可夫决策过程,并开发了一种双深度 Q 网络算法来解决它。基站被认为是一个代理,其中的状态、动作和奖励函数都经过精心设计。关于数字预编码矩阵的设计,我们开发了一种迭代加权最小均方误差算法诱导深度展开神经网络,该算法将该算法展开为具有引入可训练参数的分层结构。仿真结果验证了这种联合训练的 NN 显着优于现有的迭代算法,具有降低的复杂性和更强的鲁棒性。我们将光束选择问题表述为马尔可夫决策过程,并开发了一种双深度 Q 网络算法来解决它。基站被认为是一个代理,其中的状态、动作和奖励函数都经过精心设计。关于数字预编码矩阵的设计,我们开发了一种迭代加权最小均方误差算法诱导深度展开神经网络,该算法将该算法展开为具有引入可训练参数的分层结构。仿真结果验证了这种联合训练的 NN 显着优于现有的迭代算法,具有降低的复杂性和更强的鲁棒性。和奖励功能经过精心设计。关于数字预编码矩阵的设计,我们开发了一种迭代加权最小均方误差算法诱导深度展开神经网络,该算法将该算法展开为具有引入可训练参数的分层结构。仿真结果验证了这种联合训练的 NN 显着优于现有的迭代算法,具有降低的复杂性和更强的鲁棒性。和奖励功能经过精心设计。关于数字预编码矩阵的设计,我们开发了一种迭代加权最小均方误差算法诱导深度展开神经网络,该算法将该算法展开为具有引入可训练参数的分层结构。仿真结果验证了这种联合训练的 NN 显着优于现有的迭代算法,具有降低的复杂性和更强的鲁棒性。
更新日期:2021-07-16
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