Rapidly time-varying channel estimation for full-duplex amplify-and-forward one-way relay networks

Habib Senol, Xiaofeng Li, Cihan Tepedelenlioglu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Estimation of both cascaded and residual self-interference (RSI) channels and a new training frame structure are considered for full-duplex (FD) amplify-and-forward (AF) one-way relay networks with rapidly time-varying individual channels. To estimate the RSI and the rapidly time-varying cascaded channels, we propose a new training frame structure in which orthogonal training blocks are sent by the source node and delivered to the destination over an FD-AF relay. Exploiting the orthogonality of the training blocks, we obtain two decoupled training signal models for the estimation of the RSI and the cascaded channels. We apply linear minimum mean square error (MMSE) based estimators to the cascaded channel as well as RSI channel. In order to investigate the mean square error (MSE) performance of the system, we also derive the Bayesian Cramer-Rao lower bound. As another performance benchmark, we also assess the symbol error rate (SER) performances corresponding to the estimated and the perfect channel state information available at the receiver side. Computer simulations exhibit the proposed training frame structure and the linear MMSE estimator MSE and SER performances are shown.

Original languageEnglish (US)
Pages (from-to)3056-3069
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume66
Issue number11
DOIs
StatePublished - Jun 1 2018

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Channel estimation
Mean square error
Channel state information
Computer simulation

Keywords

  • channel estimation
  • Full duplex
  • one way relay
  • self interference
  • time varying

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Rapidly time-varying channel estimation for full-duplex amplify-and-forward one-way relay networks. / Senol, Habib; Li, Xiaofeng; Tepedelenlioglu, Cihan.

In: IEEE Transactions on Signal Processing, Vol. 66, No. 11, 01.06.2018, p. 3056-3069.

Research output: Contribution to journalArticle

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