Improving the generalized likelihood ratio test for unknown linear Gaussian channels

Elona Erez, Meir Feder

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, we consider the decoding problem for unknown Gaussian linear channels. Important examples of linear channels are the intersymbol interference (ISI) channel and the diversity channel with multiple transmit and receive antennas employing space-time codes (STC). An important class of decoders is based on the generalized likelihood ratio test (GLRT). Our work deals primarily with a decoding algorithm that uniformly improves the error probability of the GLRT decoder for these unknown linear channels. The improvement is attained by increasing the minimal distance associated with the decoder. This improvement is uniform, i.e., for all the possible channel parameters, the error probability is either smaller by a factor (that is exponential in the improved distance), or for some, may remain the same. We also present an algorithm that improves the average (over the channel parameters) error probability of the GLRT decoder. We provide simulation results for both decoders.

Original languageEnglish
Pages (from-to)919-936
Number of pages18
JournalIEEE Transactions on Information Theory
Volume49
Issue number4
DOIs
StatePublished - Apr 2003

Keywords

  • Diversity channels
  • Generalized likelihood ratio test (GLRT)
  • Intersymbol interference (ISI)
  • Maximum likelihood (ML)

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