Fluid Antenna Enabled Direction-of-Arrival Estimation Under Time-Constrained Mobility

TL;DR

This paper introduces fluid antenna structures and novel DOA estimation methods that improve accuracy and reduce complexity in time-constrained mobile wireless environments.

Contribution

It proposes specialized fluid antenna configurations and two new DOA estimation algorithms that handle aligned and non-aligned signals efficiently under mobility constraints.

Findings

Achieves underdetermined DOA estimation with minimal antenna elements.

Outperforms conventional methods in accuracy.

Reduces computational complexity significantly.

Abstract

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nystrom approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.