Multi-Objective DNN-based Precoder for MIMO Communications

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 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.