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Fluid Antenna-Assisted MU-MIMO Systems with Decentralized Baseband Processing

Tianyi Liao, Wei Guo, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief

TL;DR

The paper tackles the high computational burden of jointly optimizing beamforming and fluid antenna (FA) positions in FA-assisted MU-MIMO. It introduces a decentralized baseband processing (DBP) architecture that partitions the BS FA array into clusters, enabling parallel computation, and proposes a decentralized block coordinate ascent (BCA) algorithm based on matrix FP and MM to maximize the weighted sum-rate $R = \sum_k \alpha_k R_k$. The method achieves near-centralized WSR performance with substantial reductions in computation time (over 70%) and low communication/storage costs, validated by simulations showing convergence and scalability. This work paves the way for scalable, low-latency FA-enabled MU-MIMO in future wireless networks, with potential for distributed channel estimation and detection under DBP.

Abstract

The fluid antenna system (FAS) has emerged as a disruptive technology, offering unprecedented degrees of freedom (DoF) for wireless communication systems. However, optimizing fluid antenna (FA) positions entails significant computational costs, especially when the number of FAs is large. To address this challenge, we introduce a decentralized baseband processing (DBP) architecture to FAS, which partitions the FA array into clusters and enables parallel processing. Based on the DBP architecture, we formulate a weighted sum rate (WSR) maximization problem through joint beamforming and FA position design for FA-assisted multiuser multiple-input multiple-output (MU-MIMO) systems. To solve the WSR maximization problem, we propose a novel decentralized block coordinate ascent (BCA)-based algorithm that leverages matrix fractional programming (FP) and majorization-minimization (MM) methods. The proposed decentralized algorithm achieves low computational, communication, and storage costs, thus unleashing the potential of the DBP architecture. Simulation results show that our proposed algorithm under the DBP architecture reduces computational time by over 70% compared to centralized architectures with negligible WSR performance loss.

Fluid Antenna-Assisted MU-MIMO Systems with Decentralized Baseband Processing

TL;DR

The paper tackles the high computational burden of jointly optimizing beamforming and fluid antenna (FA) positions in FA-assisted MU-MIMO. It introduces a decentralized baseband processing (DBP) architecture that partitions the BS FA array into clusters, enabling parallel computation, and proposes a decentralized block coordinate ascent (BCA) algorithm based on matrix FP and MM to maximize the weighted sum-rate . The method achieves near-centralized WSR performance with substantial reductions in computation time (over 70%) and low communication/storage costs, validated by simulations showing convergence and scalability. This work paves the way for scalable, low-latency FA-enabled MU-MIMO in future wireless networks, with potential for distributed channel estimation and detection under DBP.

Abstract

The fluid antenna system (FAS) has emerged as a disruptive technology, offering unprecedented degrees of freedom (DoF) for wireless communication systems. However, optimizing fluid antenna (FA) positions entails significant computational costs, especially when the number of FAs is large. To address this challenge, we introduce a decentralized baseband processing (DBP) architecture to FAS, which partitions the FA array into clusters and enables parallel processing. Based on the DBP architecture, we formulate a weighted sum rate (WSR) maximization problem through joint beamforming and FA position design for FA-assisted multiuser multiple-input multiple-output (MU-MIMO) systems. To solve the WSR maximization problem, we propose a novel decentralized block coordinate ascent (BCA)-based algorithm that leverages matrix fractional programming (FP) and majorization-minimization (MM) methods. The proposed decentralized algorithm achieves low computational, communication, and storage costs, thus unleashing the potential of the DBP architecture. Simulation results show that our proposed algorithm under the DBP architecture reduces computational time by over 70% compared to centralized architectures with negligible WSR performance loss.
Paper Structure (13 sections, 38 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 13 sections, 38 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Model
  4. Problem Formulation
  5. Decentralized Block Coordinate Ascent (BCA)-Based Algorithm
  6. Decentralized Matrix Multiplication under DBP
  7. Problem Reformulation
  8. Decentralized Update of $\underline{\mathbf{\Gamma}}$ and $\underline{\mathbf{\Phi}}$
  9. Decentralized Update Step of $\underline{\mathbf{W}}$
  10. Update Step of $\underline{\mathbf{T}}$
  11. Update Step of $\underline{\mathbf{R}}$
  12. Simulation
  13. Conclusion and Future Work

Figures (5)

  • Figure 1: The system model of the FA-assisted downlink MU-MIMO with the DBP architecture at the BS.
  • Figure 2: The decentralized calculation of $\sum_{c=1}^C (\mathbf{A}^c)^\mathsf{H} \mathbf{B}^c$.
  • Figure 3: The decentralized calculation of $\underline{\mathbf{W}}$ under the DBP architecture.
  • Figure 4: The decentralized update of $\underline{\mathbf{T}}$ under the DBP architecture.
  • Figure 5: Convergence behaviors of the proposed algorithms.