Statistical learning and layered space-time architecture for point-to-point wireless communications

Mathini Sellathurai, Simon Haykin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The BLAST architecture has been proposed for high-capacity and spectrally-efficient wireless communications in an indoor environment. The method relies on multi-transmit and receive antennas to send and receive information-bearing signals in parallel. The architecture assumes a rich independent-ray scattering mechanism to make the parallel information separable at the receiving ends. In practice, with the increased number of parallel sub-streams, the scattering may be less favorable so that signal decoding algorithms are needed. In this paper, we propose a statistical learning demodulating scheme for this task.

Original languageEnglish
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
PublisherIEEE
Pages1084-1088
Number of pages5
ISBN (Print)0780351487
DOIs
Publication statusPublished - 1998

Publication series

NameConference Record of the Asilomar Conference on Signals, Systems and Computers
PublisherIEEE
ISSN (Print)1058-6393

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Statistical learning and layered space-time architecture for point-to-point wireless communications'. Together they form a unique fingerprint.

  • Cite this

    Sellathurai, M., & Haykin, S. (1998). Statistical learning and layered space-time architecture for point-to-point wireless communications. In Conference Record of the Asilomar Conference on Signals, Systems and Computers (pp. 1084-1088). (Conference Record of the Asilomar Conference on Signals, Systems and Computers). IEEE. https://doi.org/10.1109/ACSSC.1998.751429