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.

中文翻译：

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用于 MIMO 通信的基于多目标 DNN 的预编码器

本文介绍了一种基于统一深度神经网络 (DNN) 的预编码器，用于具有五个目标的双用户多输入多输出 (MIMO) 网络：数据传输、能量收集、同时无线信息和功率传输、物理层 (PHY)安全和多播。首先，开发了一种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，它在 PHY 安全和多播方面击败了现有解决方案，并且在不同的天线设置中是可靠的。接下来，基于 DNN 的预编码器旨在统一所有目标的解决方案。所提出的 DNN 同时学习传统方法给出的解决方案，即基于分析或旋转的解决方案。二元向量被设计为输入特征以区分目标。数值结果表明，与传统解决方案相比，所提出的基于 DNN 的预编码器将动态计算复杂度降低了一个数量级以上，同时达到了接近最优的性能（平均最优解的 99.45%）。新的预编码器对接收器天线数量的变化也更加稳健。