CNN-Based Channel Map Estimation for Movable Antenna Systems
Yitai Huang, Weidong Mei, Xin Wei, Zhi Chen, Boyu Ning

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…
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Taxonomy
TopicsAntenna Design and Optimization · Antenna Design and Analysis · Advanced MIMO Systems Optimization
MethodsSoftmax · Attention Is All You Need · Sparse Evolutionary Training
