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

Xintai Chen, Biqian Feng, Yongpeng Wu, Wenjun Zhang

TL;DR

The paper addresses energy efficiency optimization in a single-user MA-enhanced MIMO system under statistical CSI. It employs deterministic-equivalent reformulations of the rate with respect to transmit_covariance and APVs, coupled with an alternating-optimization framework that updates transmit variables and receive APVs via SCA and Dinkelbach methods. Key contributions include DE-based rate expressions for both transmit and receive sides, an AO algorithm with efficient inner-loop solutions (Dinkelbach for EE and water-filling for power allocation), and numerical results showing substantial EE gains over fixed-position baselines, with near-optimal performance achievable within finite movement regions. The findings demonstrate the practicality of MA deployments for energy-efficient future wireless systems, especially in large-scale MIMO settings.

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.

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

TL;DR

The paper addresses energy efficiency optimization in a single-user MA-enhanced MIMO system under statistical CSI. It employs deterministic-equivalent reformulations of the rate with respect to transmit_covariance and APVs, coupled with an alternating-optimization framework that updates transmit variables and receive APVs via SCA and Dinkelbach methods. Key contributions include DE-based rate expressions for both transmit and receive sides, an AO algorithm with efficient inner-loop solutions (Dinkelbach for EE and water-filling for power allocation), and numerical results showing substantial EE gains over fixed-position baselines, with near-optimal performance achievable within finite movement regions. The findings demonstrate the practicality of MA deployments for energy-efficient future wireless systems, especially in large-scale MIMO settings.

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.
Paper Structure (14 sections, 36 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 36 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Model
  4. Problem Formulation
  5. Design of MA-Enhanced MIMO System
  6. Deterministic Equivalent and AO Algorithm
  7. Transmit Design
  8. Inner-Layer Problem Design
  9. Receive Design
  10. Complexity and Convergence Analysis
  11. Numerical Results
  12. Impact of the Maximum Transmit Power
  13. Impact of the Movement Region Size
  14. Conclusion

Figures (2)

  • Figure 1: EE versus the maximum transmit power.
  • Figure 2: EE versus the size of the movement region.