RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

Erhan Karakoca*, Hasan Nayir*, Ali Görçin, Khalid Qaraqe*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350311143
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy
Duration: 20 Jun 202323 Jun 2023

Publication series

NameIEEE Vehicular Technology Conference
Volume2023-June
ISSN (Print)1550-2252

Conference

Conference97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/TerritoryItaly
CityFlorence
Period20/06/2323/06/23

Keywords

  • channel estimation
  • feature attention
  • generative adversarial network
  • massive MIMO
  • one-bit ADC

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