Leakage-Based Precoder Optimization and Deep Unfolding for User-Centric Cell-Free Massive MIMO

This article investigates the leakage-based precoder design for user-centric cell-free massive MIMO systems. Given the clustered APs for each UE, the problem is formulated as a group sparse optimization to maximize the leakage rate under per-AP power constraints. Its equivalent formulation is derived by introducing two auxiliary variables. An iterative optimization method (IOM) is proposed based on block coordinate descent and bisection search techniques, which converges to a stationary solution. To reduce the required computations, a deep unfolding-based method (DUBM) is developed to unfold the iterative procedure of IOM via the proposed neural network layer. Simulation results demonstrate the competitive performance of the proposed methods, with IOM achieving higher sum-rates while DUBM providing higher computational efficiency compared to the reference methods.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-12-30
DOI
https://doi.org/10.1109/tvt.2024.3524182
Primary Topic
Advanced MIMO Systems Optimization
Type
article
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article

Leakage-Based Precoder Optimization and Deep Unfolding for User-Centric Cell-Free Massive MIMO

Yi Wu, Yongming Huang, Cheng Zhang, Chunmei Xu et al.
IEEE Transactions on Vehicular Technology
Advanced MIMO Systems Optimization
article

Leakage-Based Precoder Optimization and Deep Unfolding for User-Centric Cell-Free Massive MIMO

Yi Wu, Yongming Huang, Cheng Zhang, Chunmei Xu, Zhaohua Lu
article en

Abstract

This article investigates the leakage-based precoder design for user-centric cell-free massive MIMO systems. Given the clustered APs for each UE, the problem is formulated as a group sparse optimization to maximize the leakage rate under per-AP power constraints. Its equivalent formulation is derived by introducing two auxiliary variables. An iterative optimization method (IOM) is proposed based on block coordinate descent and bisection search techniques, which converges to a stationary solution. To reduce the required computations, a deep unfolding-based method (DUBM) is developed to unfold the iterative procedure of IOM via the proposed neural network layer. Simulation results demonstrate the competitive performance of the proposed methods, with IOM achieving higher sum-rates while DUBM providing higher computational efficiency compared to the reference methods.

IEEE Transactions on Vehicular TechnologyVol. 74(5)
Fujian Normal University (CN), ZTE (China) (CN), Southeast University (CN)
Openalex Percentile: Top 77%
Advanced MIMO Systems Optimization
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Leakage-Based Precoder Optimization and Deep Unfolding for User-Centric Cell-Free Massive MIMO — Yi Wu, Yongming Huang, et al. · IEEE Transactions on Vehicular Technology (2024) | TGRS Research Map | TGRS