Joint Symbol-Level Precoding and Antenna Selection Optimization for Multiuser MIMO Systems

Symbol-level precoding (SLP) is an advanced technique designed to mitigate multi-user interference (MUI) and enhance transmission gain. This paper explores joint optimization of SLP and antenna selection to address the challenges of MUI and minimize the total system power consumption. The inherently non-convex optimization problem is approximated and refined to a convex one based on sparse estimation and sparse correction. We further reduce the number of constraints in the problem by introducing new circular constraints to replace the typical rectangular constraints. Subsequently, a coordinate descent (CD)-based iterative optimization algorithm is developed to solve the problem with a low complexity. Numerical experiments validate the effectiveness of the proposed algorithm.

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Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/wcsp62071.2024.10827091
Primary Topic
Advanced MIMO Systems Optimization
Type
article
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article

Joint Symbol-Level Precoding and Antenna Selection Optimization for Multiuser MIMO Systems

Xia Xiao, Shunqing Zhang, Xiaojing Chen, Tao Yu
Advanced MIMO Systems Optimization
article

Joint Symbol-Level Precoding and Antenna Selection Optimization for Multiuser MIMO Systems

Xia Xiao, Shunqing Zhang, Xiaojing Chen, Tao Yu
article en

Abstract

Symbol-level precoding (SLP) is an advanced technique designed to mitigate multi-user interference (MUI) and enhance transmission gain. This paper explores joint optimization of SLP and antenna selection to address the challenges of MUI and minimize the total system power consumption. The inherently non-convex optimization problem is approximated and refined to a convex one based on sparse estimation and sparse correction. We further reduce the number of constraints in the problem by introducing new circular constraints to replace the typical rectangular constraints. Subsequently, a coordinate descent (CD)-based iterative optimization algorithm is developed to solve the problem with a low complexity. Numerical experiments validate the effectiveness of the proposed algorithm.

Shanghai University (CN)
Affordable and clean energy
Openalex Percentile: Top 76%
Advanced MIMO Systems Optimization
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