Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images

Weihua Xu, Feifei Gao, Jianhua Zhang, Xiaoming Tao, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.

Original languageEnglish (US)
Pages (from-to)8145-8158
Number of pages14
JournalIEEE Transactions on Communications
Volume69
Issue number12
DOIs
StatePublished - Dec 1 2021
Externally publishedYes

Keywords

  • Deep learning
  • covariance estimation
  • location denoising
  • pilot free
  • scene image

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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