@inproceedings{19bc8b9a4d0a43079d143e29c9ed7c87,
title = "Complex-Valued Neural Network Detection for RIS-Assisted Generalized Spatial Modulation",
abstract = "Applying the deep learning in signal processing for communication systems, several models based on Real-Valued Deep Neural Network (RVDNN) and convolutional neural network (RVCNN) have been previously proposed to detect signals of generalized spatial modulation. This paper proposes a complex-valued deep neural network (CVDNN) and a complex-valued convolutional neural network (CVCNN) as detectors for reconfigurable intelligent surface (RIS)-assisted generalized spatial modulation. Contrary to previous models, the complex-valued signals are directly fed into the neural network, which requires few feature vector generators and therefore has a simpler structure. Simulation results show that the proposed CVNN detectors exhibit improved error performance and stability for various modulation schemes compared with other traditional detection schemes over Nakagami-m fading channels. The results are shown to be approaching that of maximum likelihood detection, while outperforming existing RVNN detectors.",
keywords = "BER, Nakagami-m, Reconfigurable intelligent surface, complex-valued neural network, generalized spatial modulation",
author = "Yuyan Liu and Chaorong Zhang and Ng, {Benjamin K.} and Lam, {Chan Tong}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 100th IEEE Vehicular Technology Conference, VTC 2024-Fall ; Conference date: 07-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1109/VTC2024-Fall63153.2024.10757765",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings",
address = "United States",
}