Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing

TL;DR

This paper introduces a compressed sensing-based framework for channel estimation in movable antenna systems, enabling accurate acquisition of complete channel state information by optimizing measurement positions.

Contribution

It proposes a novel joint estimation framework for multi-path parameters in MA systems and analyzes measurement position criteria for successful channel recovery.

Findings

High-accuracy channel estimation demonstrated through simulations.

Measurement position setups significantly impact estimation performance.

Framework effectively exploits multi-path structure for improved CSI acquisition.

Abstract

Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap performance gains. In this paper, we propose a general channel estimation framework for MA systems by exploiting the multi-path field response channel structure. Specifically, the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs) are jointly estimated by employing the compressed sensing method, based on multiple channel measurements at designated positions of the Tx-MA and Rx-MA. Under this framework, the Tx-MA and Rx-MA measurement positions fundamentally determine the measurement matrix for compressed sensing, of which the mutual coherence is analyzed from the perspective of Fourier transform. Moreover, two criteria for MA measurement positions are provided to guarantee the successful recovery of MPCs. Then, we propose several MA measurement position setups and compare their performance. Finally, comprehensive simulation results show that the proposed framework is able to estimate the complete CSI between the Tx and Rx regions with a high accuracy.