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Flexible Precoding for Multi-User Movable Antenna Communications

Songjie Yang, Wanting Lyu, Boyu Ning, Zhongpei Zhang, Chau Yuen

TL;DR

This letter rethinks traditional precoding in multi-user wireless communications with movable antennas with movable antennas, and proposes an off-grid regularized least squares-based orthogonal matching pursuit (RLS-OMP) method for this purpose.

Abstract

This letter rethinks traditional precoding in multi-user wireless communications with movable antennas (MAs). Utilizing MAs for optimal antenna positioning, we introduce a sparse optimization (SO)-based approach focusing on regularized zero-forcing (RZF). This framework targets the optimization of antenna positions and the precoding matrix to minimize inter-user interference and transmit power. We propose an off-grid regularized least squares-based orthogonal matching pursuit (RLS-OMP) method for this purpose. Moreover, we provide deeper insights into antenna position optimization using RLS-OMP, viewed from a subspace projection angle. Overall, our proposed flexible precoding scheme demonstrates a sum rate that exceeds more than twice that of fixed antenna positions.

Flexible Precoding for Multi-User Movable Antenna Communications

TL;DR

This letter rethinks traditional precoding in multi-user wireless communications with movable antennas with movable antennas, and proposes an off-grid regularized least squares-based orthogonal matching pursuit (RLS-OMP) method for this purpose.

Abstract

This letter rethinks traditional precoding in multi-user wireless communications with movable antennas (MAs). Utilizing MAs for optimal antenna positioning, we introduce a sparse optimization (SO)-based approach focusing on regularized zero-forcing (RZF). This framework targets the optimization of antenna positions and the precoding matrix to minimize inter-user interference and transmit power. We propose an off-grid regularized least squares-based orthogonal matching pursuit (RLS-OMP) method for this purpose. Moreover, we provide deeper insights into antenna position optimization using RLS-OMP, viewed from a subspace projection angle. Overall, our proposed flexible precoding scheme demonstrates a sum rate that exceeds more than twice that of fixed antenna positions.
Paper Structure (7 sections, 15 equations, 4 figures, 1 algorithm)

This paper contains 7 sections, 15 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. SO-Based Flexible RZF Precoding
  4. Insights Into Flexible RZF Precoding
  5. Simulation Results
  6. Conclusions
  7. Derivation of OffG Position Refinement

Figures (4)

  • Figure 1: The two-iteration antenna position optimization from the perspective of subspace projection.
  • Figure 2: CDF versus sum rate.
  • Figure 3: Sum rate versus $G$.
  • Figure 4: Sum rate versus $L$.