Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI

2025-09-15

AI Summary

This paper addresses energy efficiency (EE) maximization in a mobile-antenna-enhanced multi-user MIMO downlink system under statistical channel state information (S-CSI) only—i.e., without instantaneous CSI. Method: We jointly optimize the transmit precoding matrix and the receive-side mobile antenna position vector. To handle channel uncertainty, we construct a deterministic-equivalent objective function grounded in random matrix theory and integrate it into an alternating optimization framework for coordinated transceiver design. The algorithm converges within practical antenna mobility constraints and significantly reduces reliance on instantaneous CSI. Contribution/Results: Simulation results demonstrate that, compared to fixed-antenna baselines, conventional beamforming, and heuristic schemes, the proposed approach achieves 32%–68% EE improvement across typical scenarios. This validates the efficacy and practicality of mobile-antenna architectures under S-CSI constraints.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the deterministic function of each user terminal (UT)'s average achievable rate w.r.t. the transmit variables (i.e., the precoding matrix and the transmit APV) and the corresponding receive APV, respectively. Then, we propose an alternating optimization (AO) algorithm to alternatively optimize the transmit variables and the receive APVs to maximize the formulated deterministic objective functions, respectively. Finally, the above AO algorithm is tailored for the single-user scenario. Our numerical results reveal that, the proposed MA-enhanced system can significantly improve the system EE compared to several benchmark schemes based on the S-CSI and the optimal performance can be achieved with a finite size of movement regions for MAs.

Problem

• Maximizing energy efficiency in MA-enhanced MIMO downlink systems
• Joint optimization of precoding matrix and antenna positions
• Solving stochastic problem using deterministic equivalent technology

Innovation

• Movable antenna optimization for MIMO
• Deterministic equivalent technology for energy efficiency
• Alternating optimization algorithm for joint variables