We propose a novel antenna clustering-based method for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) full-duplex (FD) system. For a point-to-point communication set up, the proposed method enables a wireless device with multiple antennas to simultaneously transmit information and harvest energy using the same time-frequency resources. And the energy transmitting device with multiple antennas simultaneously receives information from the energy harvesting (EH) device. This is achieved by clustering the antennas into two MIMO subsystems: one for information transmission (IT) and another for EH. Furthermore, the self-interference (SI) signal at the EH device caused by the FD mode of operation is harvested by the device. For implementation-friendly antenna clustering and MIMO precoding, we propose two methods: (i) a sub-optimal method based on relaxation of objective function in a combinatorial optimization problem, and (ii) a hybrid deep reinforcement learning (DRL)-based method. For the proposed DRL solution, we design a hybrid discrete/continuous action agent that jointly clusters the MIMO antennas between EH and IT, and at the same time, find the best values for MIMO precoding matrices at both devices. This is achieved by using two interacting agent learning subsystems, namely, deep double Q-learning (DDQN), for antenna clustering and deep deterministic policy gradient (DDPG), for MIMO precoding. The effect of imperfect CSI is also studied and investigated. Finally, we study the performances of the two implementation methods and compare them with the conventional time switching-based simultaneous wireless information and power transfer (SWIPT) technique. Our findings show that the proposed MIMO clustering-based SWIPT method gives a significant improvement in spectral efficiency compared to the time switching-based SWIPT method. In particular, the DRL-based method provides the highest spectral efficiency. Besides, the numerical results show that, for the considered system set up, the number of antennas in each device should exceed three to mitigate self-interference to an acceptable level.

中文翻译：

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MIMO全双工系统中用于同时进行无线信息和功率传输的天线集群：基于深度强化学习的设计

我们为多输入多输出（MIMO）全双工（FD）系统中的同时进行无线信息和功率传输（SWIPT）提出了一种新颖的基于天线群集的方法。对于点对点通信设置，所提出的方法使具有多个天线的无线设备能够使用相同的时频资源同时发送信息并收集能量。并且具有多个天线的能量发送设备同时从能量收集（EH）设备接收信息。这是通过将天线分为两个MIMO子系统来实现的：一个用于信息传输（IT），另一个用于EH。此外，由FD操作模式引起的EH设备处的自干扰（SI）信号被该设备捕获。为了实现易于实现的天线群集和MIMO预编码，我们提出两种方法：（i）在组合优化问题中基于目标函数松弛的次优方法，以及（ii）基于混合深度强化学习（DRL）的方法。对于提出的DRL解决方案，我们设计了一种混合的离散/连续动作代理，该代理将EH和IT之间的MIMO天线联合在一起，并同时在两个设备上找到MIMO预编码矩阵的最佳值。这是通过使用两个交互的代理学习子系统实现的，即，用于天线群集的深度双Q学习（DDQN）和用于MIMO预编码的深度确定性策略梯度（DDPG）。还研究和调查了不完全CSI的影响。最后，我们研究了两种实现方法的性能，并将它们与传统的基于时间切换的同时无线信息和功率传输（SWIPT）技术进行了比较。我们的发现表明，与基于时间切换的SWIPT方法相比，所提出的基于MIMO聚类的SWIPT方法在频谱效率方面有显着提高。特别是，基于DRL的方法可提供最高的光谱效率。此外，数值结果表明，对于所考虑的系统设置，每个设备中的天线数量应超过三个，以将自干扰降低到可接受的水平。基于DRL的方法可提供最高的光谱效率。此外，数值结果表明，对于所考虑的系统设置，每个设备中的天线数量应超过三个，以将自干扰降低到可接受的水平。基于DRL的方法可提供最高的光谱效率。此外，数值结果表明，对于所考虑的系统设置，每个设备中的天线数量应超过三个，以将自干扰降低到可接受的水平。