论文标题
MIMO Communications的多目标DNN的预编码器
Multi-Objective DNN-based Precoder for MIMO Communications
论文作者
论文摘要
本文介绍了具有五个目标的两用户多输入多输出（MIMO）网络的统一的深神经网络（DNN）的预编码器：数据传输，能量收集，同时无线信息和功率传递，物理层（PHY）安全性（PHY）安全性和多播。首先，开发了基于旋转的预编码来独立解决上述问题。基于旋转的预编码是新的预编码和功率分配，它击败了PHY安全性和多播的现有解决方案，并且在不同的天线设置中可靠。 Next, a DNN-based precoder is designed to unify the solution for all objectives.拟议的DNN同时了解传统方法（即基于分析或旋转的解决方案）给出的解决方案。二进制向量被设计为可区分目标的输入功能。数值结果表明，与常规溶液相比，提出的基于DNN的预码器比在达到近乎最佳性能的同时（99.45％的平均最佳解决方案）降低了计算复杂性的降低而不是数量级。新的预编码器对接收器的天线数量的变化也更为强大。
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.
