Near-Field Communication with Massive Movable Antennas: A Functional Perspective

2025-08-02

AI Summary

This work addresses the optimal spatial deployment of large-scale reconfigurable antennas for near-field point-to-point communication, aiming to maximize the achievable rate. We propose a functional modeling framework based on continuous position and antenna density functions, transforming discrete array optimization into a variational problem over a continuous domain. First-order necessary conditions for the optimal density distribution are derived, yielding a closed-form solution under line-of-sight (LoS) conditions—revealing the critical role of higher antenna density at the array periphery for rate enhancement. Leveraging functional analysis and gradient-based optimization, we solve for the optimal density distribution under general channel conditions. A flexible array implementation scheme is further designed to mitigate mutual coupling. Simulation results demonstrate significant rate improvement over conventional configurations; notably, a uniform circular array achieves an effective trade-off between performance gain and deployment feasibility.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The advent of massive multiple-input multiple-output (MIMO) technology has provided new opportunities for capacity improvement via strategic antenna deployment, especially when the near-field effect is pronounced due to antenna proliferation. In this paper, we investigate the optimal antenna placement for maximizing the achievable rate of a point-to-point near-field channel, where the transmitter is deployed with massive movable antennas. First, we propose a novel design framework to explore the relationship between antenna positions and achievable data rate. By introducing the continuous antenna position function (APF) and antenna density function (ADF), we reformulate the antenna position design problem from the discrete to the continuous domain, which maximizes the achievable rate functional with respect to ADF. Leveraging functional analysis and variational methods, we derive the optimal ADF condition and propose a gradient-based algorithm for numerical solutions under general channel conditions. Furthermore, for the near-field line-of-sight (LoS) scenario, we present a closed-form solution for the optimal ADF, revealing the critical role of edge antenna density in enhancing the achievable rate. Finally, we propose a flexible antenna array-based deployment method that ensures practical implementation while mitigating mutual coupling issues. Simulation results demonstrate the effectiveness of the proposed framework, with uniform circular arrays emerging as a promising geometry for balancing performance and deployment feasibility in near-field communications.

Problem

• Optimizing antenna placement for near-field MIMO rate maximization
• Developing continuous domain models for antenna position design
• Balancing performance and feasibility in near-field communication arrays

Innovation

• Continuous antenna position and density functions
• Functional analysis and variational methods
• Flexible antenna array-based deployment