MMSE-based local ML detection of linearly precoded OFDM signals

L. Rugini, P. Banelli, G. B. Giannakis

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

Linear precoding is a well known effective technique to boost the performance of orthogonal frequency-division multiplexing (OFDM) systems. A drawback of linearly preceded OFDM (LP-OFDM) systems is the high computational complexity required by maximum-likelihood (ML) detection, which is mandatory to capture all the channel diversity. Conversely, low-complexity techniques, such as the linear minimum mean-squared error (MMSE) detection, suffer from non-negligible performance loss with respect to the ML performance. In this paper, we propose a detection technique that performs a local ML (LML) search in the neighborhood of the output provided by the MMSE detector. The trade-off between performance and complexity of the proposed LML-MMSE detector, which fall between the ones of the MMSE and ML detectors, can be nicely adjusted by appropriately setting the neighborhood size. Simulation results show that the LML-MMSE detector with minimum neighborhood size outperforms a block decision-feedback equalization (DFE) approach, while preserving a similar complexity.

Original languageEnglish (US)
Pages (from-to)3270-3275
Number of pages6
JournalIEEE International Conference on Communications
Volume6
StatePublished - Aug 30 2004
Event2004 IEEE International Conference on Communications - Paris, France
Duration: Jun 20 2004Jun 24 2004

Keywords

  • Linear precoding
  • Local maximum-likelihood
  • MMSE
  • OFDM

Fingerprint Dive into the research topics of 'MMSE-based local ML detection of linearly precoded OFDM signals'. Together they form a unique fingerprint.

Cite this