Movable Antenna Aided NOMA: Joint Antenna Positioning, Precoding, and Decoding Design

TL;DR

This paper explores movable antenna aided NOMA systems, jointly optimizing antenna positions, precoding, and decoding to enhance user rates, employing a novel two-loop iterative algorithm combining nature-inspired optimization and convex approximation.

Contribution

It introduces a joint optimization framework for movable antennas in NOMA systems, integrating a new two-loop algorithm with hippopotamus optimization and successive convex approximation techniques.

Findings

Significant rate improvements over fixed antenna systems.

Effective avoidance of local optima in joint variable optimization.

Enhanced system performance compared to benchmark schemes.

Abstract

This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable rate among all the users by jointly optimizing the MA positioning of each user, the precoding matrix at the BS, and the successive interference cancellation (SIC) decoding indicator matrix at the users, subject to a set of constraints including the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we propose a two-loop iterative optimization algorithm that combines the hippopotamus optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Specifically, in the inner loop, the complex-valued precoding matrix and the binary decoding indicator matrix are optimized alternatively by the successive convex approximation (SCA) technique with customized greedy search to maximize the minimum achievable rate for the given positions of the MAs. In the outer loop, each user's antenna position is updated using the HO algorithm, following a novel nature-inspired intelligent optimization framework. Simulation results show that the proposed algorithms can effectively avoid local optimum for highly coupled variables and significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.