Robust MIMO detection under imperfect CSI based on bayesian model selection

Chien Chun Cheng, S. Sezginer, Hikmet Sari, Yu-Ted Su

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A robust receiver for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. We are interested in the scenario when only a limited number of observations in both time and frequency domains are available. For this scenario, perfect channel state information is impossible to obtain and the receiver suffers from statistical information mismatch. To overcome this limitation, we first propose the optimum receiver by performing jointly channel and data estimation. For statistical information mismatch, we construct a finite set of covariance matrices and derive a model-selection scheme based on Bayesian Model Selection. Finally, the sliding-window scheme is used in order to enhance the model selection accuracy. Simulation results are presented, showing that the proposed scheme outperforms the conventional scheme under imperfect channel knowledge.

Original languageEnglish
Article number6512096
Pages (from-to)375-378
Number of pages4
JournalIEEE Wireless Communications Letters
Volume2
Issue number4
DOIs
StatePublished - 1 Jan 2013

Keywords

  • Channel estimation
  • MIMO
  • ML detection
  • OFDM

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