Performance of joint maximum-likelihood decoding for block coded signal streams in overloaded MIMO-OFDM system

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

    32 Citations (Scopus)

    Abstract

    This paper presents a joint decoding scheme for block coded signals in an overloaded multiple-input multiple-output (MIMO)-OFDM system. In a normal MIMO-OFDM system, symbol detection is carried out over the signals received by multiple antenna elements and is separated from decoding. However, owing to form factor limitation, a mobile terminal may have a limited number of receive antenna elements. Instead of splitting detection and decoding, in this paper, a joint maximum likelihood (ML) decoding in an overloaded MIMO-OFDM system is investigated. Based on ideal interleaving, independence among coded symbols spread over subcarriers is assumed. Thus, the outage capacity improves even though the ergodic capacity remains the same with a single receive antenna. As an example of the proposed system, bit error rate performance of Hamming coded and spatially multiplexed signals with joint ML decoding on a Rayleigh fading channel is investigated. The error rate is evaluated through analysis and computer simulation.

    Original languageEnglish
    Title of host publicationISPACS 2013 - 2013 International Symposium on Intelligent Signal Processing and Communication Systems
    Pages775-780
    Number of pages6
    DOIs
    Publication statusPublished - 2013 Dec 1
    Event2013 21st International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2013 - Naha, Okinawa, Japan
    Duration: 2013 Nov 122013 Nov 15

    Publication series

    NameISPACS 2013 - 2013 International Symposium on Intelligent Signal Processing and Communication Systems

    Other

    Other2013 21st International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2013
    Country/TerritoryJapan
    CityNaha, Okinawa
    Period13/11/1213/11/15

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Signal Processing

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