Fluid-Antenna-Aided Active User Detection With 1D-CNN Channel Reconstruction for Unsourced Random Access
For wireless communication systems, this work improves active user detection performance by leveraging fluid antennas and deep learning, though the gains are incremental over existing methods.
This paper applies fluid antenna systems to active user detection in unsourced random access, proposing a 1D-CNN-based channel reconstruction method that learns the mapping from partial to full channel vectors. The method achieves superior NMSE and significantly reduces AUD error rate compared to traditional approaches.
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.