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)

    Abstract

    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
    VolumeE94-A
    Issue number5
    DOIs
    Publication statusPublished - 2011 May

    Fingerprint

    Co-channel Interference
    Least Mean Square
    Interference Cancellation
    Orthogonal Frequency Division multiplexing (OFDM)
    Orthogonal frequency division multiplexing
    Maximum likelihood
    Maximum Likelihood
    Interpolation
    Antennas
    Channel estimation
    Antenna
    Mean square error
    Interference
    Interpolate
    Estimate
    Cellular Systems
    Channel Estimation
    Bandwidth
    Estimation Algorithms
    Mean Square

    Keywords

    • Interference cancellation
    • LMS-BJMLSE
    • 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

    Cite this

    @article{8a3282170a7943cb9a2bc19be83e24a2,
    title = "Training sequence reduction for the least mean square-blind joint maximum likelihood sequence estimation co-channel interference cancellation algorithm in OFDM systems",
    abstract = "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.",
    keywords = "Interference cancellation, LMS-BJMLSE, OFDM, Receiver diversity, Subcarrier identification and interpolation, Training sequence reduction",
    author = "Zhenyu Zhou and Takuro Sato",
    year = "2011",
    month = "5",
    doi = "10.1587/transfun.E94.A.1173",
    language = "English",
    volume = "E94-A",
    pages = "1173--1183",
    journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",
    issn = "0916-8508",
    publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
    number = "5",

    }

    TY - JOUR

    T1 - Training sequence reduction for the least mean square-blind joint maximum likelihood sequence estimation co-channel interference cancellation algorithm in OFDM systems

    AU - Zhou, Zhenyu

    AU - Sato, Takuro

    PY - 2011/5

    Y1 - 2011/5

    N2 - 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.

    AB - 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.

    KW - Interference cancellation

    KW - LMS-BJMLSE

    KW - OFDM

    KW - Receiver diversity

    KW - Subcarrier identification and interpolation

    KW - Training sequence reduction

    UR - http://www.scopus.com/inward/record.url?scp=79955589124&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=79955589124&partnerID=8YFLogxK

    U2 - 10.1587/transfun.E94.A.1173

    DO - 10.1587/transfun.E94.A.1173

    M3 - Article

    AN - SCOPUS:79955589124

    VL - E94-A

    SP - 1173

    EP - 1183

    JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    SN - 0916-8508

    IS - 5

    ER -