Handling Distance Constraint in Movable Antenna Aided Systems: A General Optimization Framework

TL;DR

This paper introduces a general optimization framework for movable antenna systems that effectively handles non-convex distance constraints, improving system performance in capacity and precoding tasks.

Contribution

It proposes a novel framework that separates antenna distance constraints, enabling efficient optimization without approximations, applicable to various system performance metrics.

Findings

Outperforms baseline schemes in capacity maximization

Enhances regularized zero-forcing precoding performance

Provides stationary point solutions for non-convex constraints

Abstract

The movable antenna (MA) is a promising technology to exploit more spatial degrees of freedom for enhancing wireless system performance. However, the MA-aided system introduces the non-convex antenna distance constraints, which poses challenges in the underlying optimization problems. To fill this gap, this paper proposes a general framework for optimizing the MA-aided system under the antenna distance constraints. Specifically, we separate the non-convex antenna distance constraints from the objective function by introducing auxiliary variables. Then, the resulting problem can be efficiently solved under the alternating optimization framework. For the subproblems with respect to the antenna position variables and auxiliary variables, the proposed algorithms are able to obtain at least stationary points without any approximations. To verify the effectiveness of the proposed optimization framework, we present two case studies: capacity maximization and regularized zero-forcing precoding. Simulation results demonstrate the proposed optimization framework outperforms the existing baseline schemes under both cases.