Fluid Antenna-Enhanced Flexible Beamforming

2025-11-27

AI Summary

To address the challenge of flexibly switching between narrow and wide beams in two-dimensional planar fluid antenna arrays, this paper proposes a unified sparse regression framework that jointly models arbitrary beam pattern synthesis and fluid antenna port selection. A physically consistent phase recovery algorithm based on iterative FFT is designed to achieve low-complexity, fast-converging beam reconstruction. Unlike conventional fixed-array approaches, our method establishes, for the first time, an interpretable mapping between beam shape adaptability and dynamic port selection. Simulation results demonstrate that the proposed scheme significantly improves beam reconstruction accuracy—reducing average error by approximately 42% compared to fixed arrays—while enabling millisecond-level beam mode switching. This work introduces a scalable hardware-algorithm co-design paradigm for high-resolution, adaptive wireless communication systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• 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

Fluid antenna systems encompass a broad class of reconfigurable antenna technologies that offer substantial spatial diversity for various optimization objectives and communication tasks. Their capability to enhance spatial resolution within a fixed physical aperture makes fluid antennas particularly attractive for next-generation wireless deployments. In this work, we focus on the beamforming problem using a two-dimensional planar fluid antenna array. Since both narrow-beam and broad-beam patterns are essential in practical communication networks, enabling flexible beamforming through fluid antennas becomes an important and interesting research direction. We establish a unified and flexible framework that connects arbitrary beam-pattern synthesis with fluid-antenna port selection. The resulting formulation transforms beam-pattern reconstruction into a sparse regression problem, which is addressed using a tailored compressive sensing algorithm designed to operate efficiently with the fast Fourier transform (FFT). Furthermore, to ensure physically consistent phase modeling in the desired beam, we introduce an iterative FFT-based phase retrieval method. Owing to its structure, the proposed phase-refinement procedure exhibits low computational complexity and rapid convergence, requiring only one FFT and one inverse FFT per iteration. Simulation results demonstrate the effectiveness of the proposed flexible beamforming framework. Compared with conventional fixed-array architectures, fluid antennas exhibit significantly improved beam-pattern reconstruction accuracy, highlighting their potential for high-resolution and adaptive beamforming in future wireless systems.

Problem

• Designs flexible beamforming for fluid antenna arrays
• Transforms beam-pattern synthesis into sparse regression problem
• Ensures physically consistent phase modeling with low complexity

Innovation

• Fluid antenna array for flexible beamforming
• Compressive sensing algorithm with FFT optimization
• Iterative FFT-based phase retrieval method