Rician K-Factor Estimation Using Deep Learning

Mofadal Alymani, Mohsen H. Alhazmi, Alhussain Almarhabi, Hatim Alhazmi, Abdullah Samarkandi, Yu Dong Yao

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

15 Scopus citations

Abstract

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.

Original languageEnglish
Title of host publication2020 29th Wireless and Optical Communications Conference, WOCC 2020
ISBN (Electronic)9781728161242
DOIs
StatePublished - May 2020
Event29th Wireless and Optical Communications Conference, WOCC 2020 - Newark, United States
Duration: 1 May 20202 May 2020

Publication series

Name2020 29th Wireless and Optical Communications Conference, WOCC 2020

Conference

Conference29th Wireless and Optical Communications Conference, WOCC 2020
Country/TerritoryUnited States
CityNewark
Period1/05/202/05/20

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