Fluid-Antenna-Aided Active User Detection With 1D-CNN Channel Reconstruction for Unsourced Random Access

2026-06-30

AI Summary

This work addresses the performance limitations of active user detection (AUD) in unsourced random access (URA) caused by incomplete channel state information. To overcome this challenge, the paper introduces a novel approach that integrates fluid antenna systems with a one-dimensional convolutional neural network (1D-CNN) to reconstruct full channel vectors from partial channel observations in a data-driven manner. Based on the reconstructed channels, the method optimizes antenna port selection to enhance system performance. Experimental results demonstrate significant improvements in both channel estimation accuracy and AUD reliability: across various pilot lengths, the proposed scheme achieves substantially lower normalized mean square error (NMSE) in channel reconstruction and markedly reduced active user detection error rates compared to conventional methods.

Abstract

In this paper, we investigate the application of fluid antenna systems (FAS) for active user detection (AUD) in unsourced random access (URA). A channel reconstruction method based on a one-dimensional convolutional neural network (1D-CNN) is proposed to effectively learn the nonlinear mapping from partial channel observations to the full channel vector. Furthermore, the reconstructed channel information is exploited to improve AUD performance via port selection. Simulation results demonstrate that the proposed 1D-CNN channel reconstructor significantly outperforms traditional methods under varying pilot lengths, achieving superior normalized mean squared error (NMSE) performance. Additionally, the reconstructed channel substantially reduces the AUD error rate compared with conventional approaches relying on traditional antenna configurations.

Problem

• Unsourced Random Access
• Active User Detection
• Fluid Antenna Systems
• Channel Reconstruction

Innovation

• Fluid Antenna System
• 1D-CNN
• Channel Reconstruction
• Active User Detection
• Unsourced Random Access