@inproceedings{3b6aae97142d48d5968f80edbb2eb087,
title = "RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems",
abstract = "The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.",
keywords = "channel estimation, feature attention, generative adversarial network, massive MIMO, one-bit ADC",
author = "Erhan Karakoca and Hasan Nayir and Ali G{\"o}r{\c c}in and Khalid Qaraqe",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/VTC2023-Spring57618.2023.10199774",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings",
address = "United States",
}