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Rotatable Antenna Enhanced Multicast Communication System

Weihua Zhu, Beixiong Zheng, Lipeng Zhu, Jie Tang, Yong Zeng

Abstract

Rotatable antenna (RA) provides additional spatial degrees of freedom (DoFs) for communication systems by enabling per-antenna dynamic boresight adjustment, which is attractive for fairness-oriented multicast transmission. This letter investigates an RA-enhanced downlink multi-group multicast system. Specifically, we aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the multicast beamforming vectors and the RA boresight directions under transmit power and rotation constraints. To solve this non-convex problem, we first reformulate the max-min SINR objective via quadratic transform. Then, we develop an alternating optimization (AO) algorithm that iteratively updates the multicast beamforming and RA boresight directions. The beamforming vectors are obtained from a convex subproblem, while the boresight directions are refined using a successive convex approximation (SCA) procedure. Simulation results verify that the proposed RA-based scheme substantially enhances the fairness performance compared with fixed antenna-based and random-orientation benchmarks.

Rotatable Antenna Enhanced Multicast Communication System

Abstract

Rotatable antenna (RA) provides additional spatial degrees of freedom (DoFs) for communication systems by enabling per-antenna dynamic boresight adjustment, which is attractive for fairness-oriented multicast transmission. This letter investigates an RA-enhanced downlink multi-group multicast system. Specifically, we aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the multicast beamforming vectors and the RA boresight directions under transmit power and rotation constraints. To solve this non-convex problem, we first reformulate the max-min SINR objective via quadratic transform. Then, we develop an alternating optimization (AO) algorithm that iteratively updates the multicast beamforming and RA boresight directions. The beamforming vectors are obtained from a convex subproblem, while the boresight directions are refined using a successive convex approximation (SCA) procedure. Simulation results verify that the proposed RA-based scheme substantially enhances the fairness performance compared with fixed antenna-based and random-orientation benchmarks.

Paper Structure

This paper contains 13 sections, 27 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Algorithm
  6. Conversion of Original Problem
  7. Optimization for Beamforming Vectors
  8. Optimization for Pointing Vectors
  9. Overall Algorithm
  10. Simulation Results
  11. Conclusion
  12. Construction of L-{k}Ŝ
  13. Construction of L-{k,j}Î

Figures (3)

  • Figure 1: Illustration of system model, $\theta_{z,n}$ and $\theta_{a,n}$.
  • Figure 2: (a) The top view of simulation setup. (b) Convergence behavior of the proposed AO algorithm.
  • Figure 3: (a) Average max-min SINR versus the maximum transmit power at the BS. (b) Average max-min SINR versus the user distribution angle phi. (c) Average max-min SINR versus the number of antennas at the BS.