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6DMA-Aided Cell-Free Massive MIMO Communication

Xiaoming Shi, Xiaodan Shao, Beixiong Zheng, Rui Zhang

TL;DR

The paper tackles the throughput limitations of cell-free mMIMO by equipping APs with 6DMA surfaces that can rotate on circular tracks to exploit macro spatial diversity. It formulates a non-convex rotation-optimization problem to maximize the average sum-rate, approximated via Monte Carlo over a non-homogeneous user distribution, and solves it using a Bayesian optimization framework with a Gaussian process surrogate and an expected-improvement acquisition function. The proposed approach demonstrates significant gains over fixed-position antenna systems and centralized 6DMA deployments, with performance advantages enhanced when the user distribution is spatially diverse and depending on the level of CSI cooperation (local vs. global). The work highlights the value of joint, topology-aware surface rotations across distributed APs to tailor antenna resources to users’ spatial distributions in 6G-like networks.

Abstract

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit (CPU) via fronthaul links, 6DMA-APs can optimize their combining vectors for decoding the users' information based on either local channel state information (CSI) or global CSI shared among them. We aim to maximize the average achievable sum-rate via jointly optimizing the rotation angles of all 6DMA surfaces at all APs, based on the users' spatial distribution. Since the formulated problem is non-convex and highly non-linear, we propose a Bayesian optimization-based algorithm to solve it efficiently. Simulation results show that, by enhancing signal power and mitigating interference through reduced channel cross-correlation among users, 6DMA-APs with optimized rotations can significantly improve the average sum-rate, as compared to the conventional cell-free network with fixed-position antennas and that with only a single centralized AP with optimally rotated 6DMAs, especially when the user distribution is more spatially diverse.

6DMA-Aided Cell-Free Massive MIMO Communication

TL;DR

The paper tackles the throughput limitations of cell-free mMIMO by equipping APs with 6DMA surfaces that can rotate on circular tracks to exploit macro spatial diversity. It formulates a non-convex rotation-optimization problem to maximize the average sum-rate, approximated via Monte Carlo over a non-homogeneous user distribution, and solves it using a Bayesian optimization framework with a Gaussian process surrogate and an expected-improvement acquisition function. The proposed approach demonstrates significant gains over fixed-position antenna systems and centralized 6DMA deployments, with performance advantages enhanced when the user distribution is spatially diverse and depending on the level of CSI cooperation (local vs. global). The work highlights the value of joint, topology-aware surface rotations across distributed APs to tailor antenna resources to users’ spatial distributions in 6G-like networks.

Abstract

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit (CPU) via fronthaul links, 6DMA-APs can optimize their combining vectors for decoding the users' information based on either local channel state information (CSI) or global CSI shared among them. We aim to maximize the average achievable sum-rate via jointly optimizing the rotation angles of all 6DMA surfaces at all APs, based on the users' spatial distribution. Since the formulated problem is non-convex and highly non-linear, we propose a Bayesian optimization-based algorithm to solve it efficiently. Simulation results show that, by enhancing signal power and mitigating interference through reduced channel cross-correlation among users, 6DMA-APs with optimized rotations can significantly improve the average sum-rate, as compared to the conventional cell-free network with fixed-position antennas and that with only a single centralized AP with optimally rotated 6DMAs, especially when the user distribution is more spatially diverse.

Paper Structure

This paper contains 11 sections, 15 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. 6DMA-AP Architecture
  4. Channel Model
  5. Signal Model
  6. Problem Formulation
  7. Proposed Algorithm
  8. GP-Based Surrogate Model
  9. EI-Based Acquisition Function
  10. Simulation Results
  11. Conclusions

Figures (5)

  • Figure 1: Illustration of a 6DMA-aided cell-free network with a set of distributed 6DMA-APs connected to the CPU.
  • Figure 2: Geometry of the 6DMA-AP.
  • Figure 3: Optimized rotations of 6DMA surfaces using the CMMSE and LMMSE receiver combining methods.
  • Figure 4: Average achievable sum-rate versus user density ratio with different receiver combining methods.
  • Figure 5: Average achievable sum-rate versus average number of users with different receiver combining methods.