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

Zhenyu Xiao, Zhe Li, Lipeng Zhu, Boyu Ning, Daniel Benevides da Costa, Xiang-Gen Xia, Rui Zhang

TL;DR

This work addresses downlink MA-aided NOMA by jointly optimizing MA positions, BS precoding, and adaptive SIC decoding to maximize the minimum user rate under movement and power constraints. It introduces a two-loop HO-AO optimization framework: an inner AO loop that uses SCA and greedy search to optimize the precoding and decoding indicators for fixed MA positions, and an outer HO loop that updates MA positions to escape local optima. The approach yields substantial rate improvements over conventional fixed-antenna schemes and various benchmarks, and demonstrates robustness under imperfect field-response information. The findings highlight the potential of MA-enabled spatial diversity and adaptive decoding to enhance spectral efficiency in future 6G-type networks.

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.

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

TL;DR

This work addresses downlink MA-aided NOMA by jointly optimizing MA positions, BS precoding, and adaptive SIC decoding to maximize the minimum user rate under movement and power constraints. It introduces a two-loop HO-AO optimization framework: an inner AO loop that uses SCA and greedy search to optimize the precoding and decoding indicators for fixed MA positions, and an outer HO loop that updates MA positions to escape local optima. The approach yields substantial rate improvements over conventional fixed-antenna schemes and various benchmarks, and demonstrates robustness under imperfect field-response information. The findings highlight the potential of MA-enabled spatial diversity and adaptive decoding to enhance spectral efficiency in future 6G-type networks.

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.

Paper Structure

This paper contains 17 sections, 35 equations, 9 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Model
  4. Adaptive SIC Decoding and Achievable Rate
  5. Problem Formulation
  6. Proposed Solution
  7. Precoding Matrix Optimization
  8. Decoding Indicator Matrix Optimization
  9. AO Algorithm
  10. MA Position Optimization
  11. Computational Complexity
  12. Simulation Results
  13. Simulation Setup
  14. Convergence Performance of Proposed Algorithms
  15. Performance Comparison with Benchmark Schemes
  16. ...and 2 more sections

Figures (9)

  • Figure 1: Illustration of the downlink NOMA transmission with adaptive decoding between the BS and $K$ single-MA users.
  • Figure 2: Evaluation of the convergence of the proposed improved HO algorithm and the original HO algorithm.
  • Figure 3: Minimum achievable rates for different schemes versus number of users.
  • Figure 4: Minimum achievable rates for different schemes versus number of transmit antennas.
  • Figure 5: Minimum achievable rates for different schemes versus number of paths.
  • ...and 4 more figures