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Capacity Maximization for Base Station with Hybrid Fixed and Movable Antennas

Xiaoming Shi, Xiaodan Shao, Rui Zhang

TL;DR

This work addresses capacity maximization for a base station architecture that hybrids fixed-position arrays with movable 6DMA surfaces (HFMA). It formulates the problem as discrete rotation-angle optimization to match nonuniform user distributions and tackles the combinatorial nature with an adaptive Markov Chain Monte Carlo method using a Metropolized independence sampler. The proposed AMCMC algorithm demonstrates near-optimal performance with substantially lower complexity than exhaustive search, yielding significant ASE gains over benchmark schemes, particularly in hotspot-rich scenarios. The results suggest HFMA-BS with optimized 6DMA rotations offers a practical route to large-scale capacity improvements in future wireless networks while limiting deployment costs.

Abstract

Six-dimensional movable antenna (6DMA) is an effective solution for enhancing wireless network capacity through the adjustment of both 3D positions and 3D rotations of distributed antennas/antenna surfaces. Although freely positioning/rotating 6DMA surfaces offers the greatest flexibility and thus highest capacity improvement, its implementation may be challenging in practice due to the drastic architecture change required for existing base stations (BSs), which predominantly adopt fixed-position antenna (FPA) arrays (e.g., sector antenna arrays). Thus, we introduce in this letter a new BS architecture called hybrid fixed and movable antennas (HFMA), which consists of both conventional FPA arrays and position/rotation-adjustable 6DMA surfaces. For ease of implementation, we consider that all 6DMA surfaces can rotate along a circular track above the FPA arrays. We aim to maximize the network capacity via optimizing the rotation angles of all 6DMA surfaces based on the users' spatial distribution. Since this problem is combinatorial and its optimal solution requires prohibitively high computational complexity via exhaustive search, we propose an alternative adaptive Markov Chain Monte Carlo based method to solve it more efficiently. Finally, we present simulation results that show significant performance gains achieved by our proposed design over various benchmark schemes.

Capacity Maximization for Base Station with Hybrid Fixed and Movable Antennas

TL;DR

This work addresses capacity maximization for a base station architecture that hybrids fixed-position arrays with movable 6DMA surfaces (HFMA). It formulates the problem as discrete rotation-angle optimization to match nonuniform user distributions and tackles the combinatorial nature with an adaptive Markov Chain Monte Carlo method using a Metropolized independence sampler. The proposed AMCMC algorithm demonstrates near-optimal performance with substantially lower complexity than exhaustive search, yielding significant ASE gains over benchmark schemes, particularly in hotspot-rich scenarios. The results suggest HFMA-BS with optimized 6DMA rotations offers a practical route to large-scale capacity improvements in future wireless networks while limiting deployment costs.

Abstract

Six-dimensional movable antenna (6DMA) is an effective solution for enhancing wireless network capacity through the adjustment of both 3D positions and 3D rotations of distributed antennas/antenna surfaces. Although freely positioning/rotating 6DMA surfaces offers the greatest flexibility and thus highest capacity improvement, its implementation may be challenging in practice due to the drastic architecture change required for existing base stations (BSs), which predominantly adopt fixed-position antenna (FPA) arrays (e.g., sector antenna arrays). Thus, we introduce in this letter a new BS architecture called hybrid fixed and movable antennas (HFMA), which consists of both conventional FPA arrays and position/rotation-adjustable 6DMA surfaces. For ease of implementation, we consider that all 6DMA surfaces can rotate along a circular track above the FPA arrays. We aim to maximize the network capacity via optimizing the rotation angles of all 6DMA surfaces based on the users' spatial distribution. Since this problem is combinatorial and its optimal solution requires prohibitively high computational complexity via exhaustive search, we propose an alternative adaptive Markov Chain Monte Carlo based method to solve it more efficiently. Finally, we present simulation results that show significant performance gains achieved by our proposed design over various benchmark schemes.
Paper Structure (9 sections, 17 equations, 4 figures, 1 algorithm)

This paper contains 9 sections, 17 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. HFMA-BS
  4. Channel Model
  5. User Distribution
  6. Problem Formulation
  7. Proposed Algorithm
  8. Simulation Results
  9. Conclusion

Figures (4)

  • Figure 1: Illustration of the HFMA-BS and the geometries of FPA array and 6DMA surface.
  • Figure 2: ASE versus transmit power per user with $L=20$, $M_{{\rm MA}}=2\!\times\!2$, $M_{{\rm FA}}=4\!\times\!4$ and $\bar{K}=75$.
  • Figure 3: Optimized rotations of 6DMA surfaces by the AMCMC-based algorithm.
  • Figure 4: ASE versus transmit power per user for different schemes with different $\bar{K}$.