基于多目标DNN的MIMO通信预编码器
Multi-Objective DNN-based Precoder for MIMO Communications
摘要：
本文介绍了一种基于统一深度神经网络（DNN）的预编码器，用于两用户多输入多输出（MIMO）网络，具有五个目标：数据传输，能量收集，同时无线信息和功率传输，物理层（PHY）安全性和多播。首先，开发了基于旋转的预编码来独立地解决上述问题。基于旋转的预编码是一种新的预编码和功率分配，它在PHY安全和多播方面优于现有的解决方案，并且在不同的天线设置下都是可靠的。接下来，设计了一个基于DNN的预编码器来统一所有目标的解决方案。所提出的DNN同时学习由传统方法给出的解决方案，即，解析或基于旋转的解决方案。一个二进制向量被设计为一个输入功能，以区分目标。数值结果表明，与传统的解决方案相比，所提出的基于DNN的预编码器降低了飞行中的计算复杂性超过一个数量级，同时达到接近最优的性能（平均最优解的99.45%）。新的预编码器也是更强大的接收机的天线数量的变化。
英文摘要：
This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoding is developed to solve the above problems independently. Rotation-based precoding is new precoding and power allocation that beats existing solutions in PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45\% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers.
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