Multi-Objective DNN-based Precoder for MIMO Communications

• URL: http://arxiv.org/abs/2007.02896v1
• Date: Mon, 6 Jul 2020 17:20:46 GMT
• Title: Multi-Objective DNN-based Precoder for MIMO Communications
• Authors: Xinliang Zhang, Mojtaba Vaezi
• Abstract summary: This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks. The proposed precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance.
• Score: 24.232286402470535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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