Multi-Objective DNN-based Precoder for MIMO Communications
Xinliang Zhang, Mojtaba Vaezi

TL;DR
This paper proposes a unified deep neural network-based precoder for MIMO networks that efficiently balances multiple objectives, outperforming traditional methods in complexity and robustness.
Contribution
It introduces a novel rotation-based precoding method and a DNN model that jointly optimize multiple objectives in MIMO systems, enhancing performance and computational efficiency.
Findings
DNN precoder achieves 99.45% of optimal performance.
Reduces computational complexity by over an order of magnitude.
Improves robustness to antenna configuration variations.
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…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Advanced MIMO Systems Optimization · Antenna Design and Analysis
