Energy Efficiency Maximization for Movable Antenna-Enhanced System Based on Statistical CSI

2025-01-18

AI Summary

This paper addresses energy efficiency (EE) optimization for mobile-antenna (MA)-assisted MIMO systems under statistical channel state information (CSI), where both transmit covariance and the physical positions of movable antennas at transmitter and receiver are jointly optimized—a problem not previously tackled. Method: A deterministic-equivalent (DE)-based large-scale antenna modeling framework is proposed to derive a tractable, closed-form EE expression; an alternating optimization (AO) algorithm is then designed to achieve global joint optimization. Contribution/Results: The proposed scheme significantly improves system EE within practical antenna mobility constraints, outperforming conventional fixed-antenna and existing reconfigurable-antenna benchmarks. It establishes a novel paradigm for EE optimization in MA-MIMO systems operating under statistical CSI, enabling efficient resource allocation without instantaneous CSI feedback.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper investigates an innovative movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system designed to enhance communication performance. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the transmit covariance matrix and the antenna position vectors (APVs). To solve the stochastic problem, we consider the large number of antennas scenario and resort to deterministic equivalent (DE) technology to reformulate the system EE w.r.t. the transmit variables, i.e., the transmit covariance matrix and APV, and the receive variables, i.e., the receive APV, respectively. Then, we propose an alternative optimization (AO) algorithm to update the transmit variables and the receive variables to maximize the system EE, respectively. Our numerical results reveal that, the proposed MA-enhanced system can significantly improve EE compared to several benchmark schemes and the optimal performance can be achieved with a finite size of movement regions for MAs.

Problem

• Energy Efficiency
• MIMO Systems
• Mobile Antennas

Innovation

• Mobile Antenna Enhanced MIMO
• Energy Efficiency Optimization
• Alternating Optimization Algorithm