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Joint Transmitter and Receiver Design for Movable Antenna Enhanced Multicast Communications

Ying Gao, Qingqing Wu, Wen Chen

TL;DR

This work tackles multicast downlink design with movable antennas (MAs) at both the base station and user terminals. It formulates a max-min weighted SINR problem over beamformers and MA positions, and solves it via an alternating optimization framework that employs a second-order Taylor-based concave surrogate to bound SNR, enabling a single-step approximation instead of a two-step method. The single-group problem is extended to a multi-group setting with slack variables to preserve min-Fairness, and the resulting AO-SCA algorithm is shown to converge with favorable complexity. Simulations demonstrate that joint MA positioning and beamforming can yield up to 3.4% faster convergence and substantial SNR/SINR gains over benchmarks, with receive-MA schemes often outperforming transmit-MA ones in overloaded or multi-group scenarios. The results provide practical guidance on MA deployment for multicast and highlight potential synergies with other reconfigurable technologies.

Abstract

Movable antenna (MA) is an emerging technology that utilizes localized antenna movement to achieve better channel conditions for enhancing communication performance. In this paper, we study the MA-enhanced multicast transmission from a base station equipped with multiple MAs to multiple groups of single-MA users. Our goal is to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) among all the users by jointly optimizing the position of each transmit/receive MA and the transmit beamforming. To tackle this challenging problem, we first consider the single-group scenario and propose an efficient algorithm based on the techniques of alternating optimization and successive convex approximation. Particularly, when optimizing transmit or receive MA positions, we construct a concave lower bound for the signal-to-noise ratio (SNR) of each user using only the second-order Taylor expansion, which simplifies the problem-solving process compared to the existing two-step approximation method. The proposed design is then extended to the general multi-group scenario. Simulation results show that the proposed algorithm converges faster than the existing two-step approximation method, achieving a 3.4% enhancement in max-min SNR. Moreover, it can improve the max-min SNR/SINR by up to 22.5%, 181.7%, and 343.9% compared to benchmarks employing only receive MAs, only transmit MAs, and both transmit and receive FPAs, respectively.

Joint Transmitter and Receiver Design for Movable Antenna Enhanced Multicast Communications

TL;DR

This work tackles multicast downlink design with movable antennas (MAs) at both the base station and user terminals. It formulates a max-min weighted SINR problem over beamformers and MA positions, and solves it via an alternating optimization framework that employs a second-order Taylor-based concave surrogate to bound SNR, enabling a single-step approximation instead of a two-step method. The single-group problem is extended to a multi-group setting with slack variables to preserve min-Fairness, and the resulting AO-SCA algorithm is shown to converge with favorable complexity. Simulations demonstrate that joint MA positioning and beamforming can yield up to 3.4% faster convergence and substantial SNR/SINR gains over benchmarks, with receive-MA schemes often outperforming transmit-MA ones in overloaded or multi-group scenarios. The results provide practical guidance on MA deployment for multicast and highlight potential synergies with other reconfigurable technologies.

Abstract

Movable antenna (MA) is an emerging technology that utilizes localized antenna movement to achieve better channel conditions for enhancing communication performance. In this paper, we study the MA-enhanced multicast transmission from a base station equipped with multiple MAs to multiple groups of single-MA users. Our goal is to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) among all the users by jointly optimizing the position of each transmit/receive MA and the transmit beamforming. To tackle this challenging problem, we first consider the single-group scenario and propose an efficient algorithm based on the techniques of alternating optimization and successive convex approximation. Particularly, when optimizing transmit or receive MA positions, we construct a concave lower bound for the signal-to-noise ratio (SNR) of each user using only the second-order Taylor expansion, which simplifies the problem-solving process compared to the existing two-step approximation method. The proposed design is then extended to the general multi-group scenario. Simulation results show that the proposed algorithm converges faster than the existing two-step approximation method, achieving a 3.4% enhancement in max-min SNR. Moreover, it can improve the max-min SNR/SINR by up to 22.5%, 181.7%, and 343.9% compared to benchmarks employing only receive MAs, only transmit MAs, and both transmit and receive FPAs, respectively.
Paper Structure (23 sections, 41 equations, 9 figures, 1 algorithm)

This paper contains 23 sections, 41 equations, 9 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. MA-Enabled Systems
  4. Multicast Systems
  5. Motivation and Contributions
  6. Organization and Notations
  7. System Model and Problem Formulation
  8. System Model
  9. Problem Formulation
  10. Single-Group Scenario
  11. Optimizing $\bm w$ for Given $\left\lbrace \{\bm t_m\},\{\bm r_k\}\right\rbrace$
  12. Optimizing $\bm t_m$ for Given $\left\lbrace \bm w, \{\bm t_p\}_{p\in\mathcal{M}\setminus\{m\}}, \{\bm r_k\}\right\rbrace$
  13. Optimizing $\{\bm r_k\}$ for Given $\left\lbrace \bm w, \{\bm t_m\}\right\rbrace$
  14. Overall Algorithm: Convergence and Complexity Analysis
  15. Multi-Group Scenario
  16. ...and 8 more sections

Figures (9)

  • Figure 1: Illustration of an MA-enhanced multi-group multicast MISO communication system.
  • Figure 2: Convergence behaviors of the proposed Algorithm \ref{['Alg1']} and the existing method in 2023_Wenyan_MIMO. (a) Average max-min SNR versus the iteration index; (b) The corresponding fractional increase of the max-min SNR versus the iteration index.
  • Figure 3: Average max-min SNR versus the normalized region size.
  • Figure 4: Average max-min SNR versus the number of paths for each user.
  • Figure 5: Average max-min SNR versus the maximum transmit power at the BS.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2