CNN-Based Channel Map Estimation for Movable Antenna Systems

TL;DR

This paper introduces a CNN-based method to efficiently reconstruct 3D small-scale channel maps for movable antenna systems, reducing the need for exhaustive measurements and enabling better wireless channel reconfiguration.

Contribution

It proposes a novel CNN-based scheme to accurately estimate the entire 3D channel map from limited measurements in movable antenna systems, addressing a key challenge in wireless channel estimation.

Findings

The CNN scheme accurately reconstructs the channel map.

It outperforms benchmark estimation schemes.

The method reduces measurement overhead significantly.

Abstract

Movable antenna (MA) has attracted increasing attention in wireless communications due to its capability of wireless channel reconfiguration through local antenna movement within a confined region at the transmitter/receiver. However, to determine the optimal antenna positions, channel state information (CSI) within the entire region, termed small-scale channel map, is required, which poses a significant challenge due to the unaffordable overhead for exhaustive channel estimation at all positions. To tackle this challenge, in this paper, we propose a new convolutional neural network (CNN)-based estimation scheme to reconstruct the small-scale channel map within a three-dimensional (3D) movement region. Specifically, we first collect a set of CSI measurements corresponding to a subset of MA positions and different receiver locations offline to comprehensively capture the environmental features. Subsequently, we train a CNN using the collected data, which is then used to reconstruct the full channel map during real-time transmission only based on a finite number of channel measurements taken at several selected MA positions within the 3D movement region. Numerical results demonstrate that our proposed scheme can accurately reconstruct the small-scale channel map and outperforms other benchmark schemes.