Training sequence reduction for the least mean square-blind joint maximum likelihood sequence estimation co-channel interference cancellation algorithm in OFDM systems

Zhenyu Zhou, Takuro Sato

    Research output: Contribution to journalArticle

    2 Citations (Scopus)


    Due to the reuse factor reduction, the attendant increase in co-channel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean squareblind joint maximum likelihood sequence estimation (LMS-BJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

    Original languageEnglish
    Pages (from-to)1173-1183
    Number of pages11
    JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    Issue number5
    Publication statusPublished - 2011 May



    • Interference cancellation
    • OFDM
    • Receiver diversity
    • Subcarrier identification and interpolation
    • Training sequence reduction

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Computer Graphics and Computer-Aided Design
    • Applied Mathematics
    • Signal Processing

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