A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden markov models

David Jonathan Miller, Moonseo Park

    Research output: Contribution to journalArticle

    46 Citations (Scopus)

    Abstract

    In previous work on source coding over noisy channels it was recognized that when the source has memory, there is typically residual redundancy between the discrete symbols produced by the encoder, which can be capitalized upon by the decoder to improve the overall quantizer performance. Sayood and Borkenhagen and Phamdo and Farvardin proposed detectors at the decoder which optimize suitable criteria in order to estimate the sequence of transmitted symbols. Phamdo and Farvardin also proposed an instantaneous approximate minimum mean-squared error (IAMMSE) decoder. These methods provide a performance advantage over conventional systems, but the maximum a posteriori (MAP) structure is suboptimal, while the IAMMSE decoder makes limited use of the redundancy. Alternatively, combining aspects of both approaches, we propose a sequence-based approximate MMSE (SAMMSE) decoder. For a Markovian sequence of encoder-produced symbols and a discrete memoryless channel, we approximate the expected distortion at the decoder under the constraint of fixed decoder complexity. For this simplified cost, the optimal decoder computes expected values based on a discrete hidden Markov model, using the wellknown forward/backward (F/B) algorithm. Performance gains for this scheme are demonstrated over previous techniques in quantizing Gauss-Markov sources over a range of noisy channel conditions. Moreover, a constrained delay version is also suggested.

    Original languageEnglish (US)
    Pages (from-to)222-231
    Number of pages10
    JournalIEEE Transactions on Communications
    Volume46
    Issue number2
    DOIs
    StatePublished - Jan 1 1998

    Fingerprint

    Hidden Markov models
    Redundancy
    Detectors
    Data storage equipment
    Costs

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    @article{37263905434344d1b49b5981a411d79b,
    title = "A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden markov models",
    abstract = "In previous work on source coding over noisy channels it was recognized that when the source has memory, there is typically residual redundancy between the discrete symbols produced by the encoder, which can be capitalized upon by the decoder to improve the overall quantizer performance. Sayood and Borkenhagen and Phamdo and Farvardin proposed detectors at the decoder which optimize suitable criteria in order to estimate the sequence of transmitted symbols. Phamdo and Farvardin also proposed an instantaneous approximate minimum mean-squared error (IAMMSE) decoder. These methods provide a performance advantage over conventional systems, but the maximum a posteriori (MAP) structure is suboptimal, while the IAMMSE decoder makes limited use of the redundancy. Alternatively, combining aspects of both approaches, we propose a sequence-based approximate MMSE (SAMMSE) decoder. For a Markovian sequence of encoder-produced symbols and a discrete memoryless channel, we approximate the expected distortion at the decoder under the constraint of fixed decoder complexity. For this simplified cost, the optimal decoder computes expected values based on a discrete hidden Markov model, using the wellknown forward/backward (F/B) algorithm. Performance gains for this scheme are demonstrated over previous techniques in quantizing Gauss-Markov sources over a range of noisy channel conditions. Moreover, a constrained delay version is also suggested.",
    author = "Miller, {David Jonathan} and Moonseo Park",
    year = "1998",
    month = "1",
    day = "1",
    doi = "10.1109/26.659481",
    language = "English (US)",
    volume = "46",
    pages = "222--231",
    journal = "IEEE Transactions on Communications",
    issn = "0096-1965",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    number = "2",

    }

    A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden markov models. / Miller, David Jonathan; Park, Moonseo.

    In: IEEE Transactions on Communications, Vol. 46, No. 2, 01.01.1998, p. 222-231.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden markov models

    AU - Miller, David Jonathan

    AU - Park, Moonseo

    PY - 1998/1/1

    Y1 - 1998/1/1

    N2 - In previous work on source coding over noisy channels it was recognized that when the source has memory, there is typically residual redundancy between the discrete symbols produced by the encoder, which can be capitalized upon by the decoder to improve the overall quantizer performance. Sayood and Borkenhagen and Phamdo and Farvardin proposed detectors at the decoder which optimize suitable criteria in order to estimate the sequence of transmitted symbols. Phamdo and Farvardin also proposed an instantaneous approximate minimum mean-squared error (IAMMSE) decoder. These methods provide a performance advantage over conventional systems, but the maximum a posteriori (MAP) structure is suboptimal, while the IAMMSE decoder makes limited use of the redundancy. Alternatively, combining aspects of both approaches, we propose a sequence-based approximate MMSE (SAMMSE) decoder. For a Markovian sequence of encoder-produced symbols and a discrete memoryless channel, we approximate the expected distortion at the decoder under the constraint of fixed decoder complexity. For this simplified cost, the optimal decoder computes expected values based on a discrete hidden Markov model, using the wellknown forward/backward (F/B) algorithm. Performance gains for this scheme are demonstrated over previous techniques in quantizing Gauss-Markov sources over a range of noisy channel conditions. Moreover, a constrained delay version is also suggested.

    AB - In previous work on source coding over noisy channels it was recognized that when the source has memory, there is typically residual redundancy between the discrete symbols produced by the encoder, which can be capitalized upon by the decoder to improve the overall quantizer performance. Sayood and Borkenhagen and Phamdo and Farvardin proposed detectors at the decoder which optimize suitable criteria in order to estimate the sequence of transmitted symbols. Phamdo and Farvardin also proposed an instantaneous approximate minimum mean-squared error (IAMMSE) decoder. These methods provide a performance advantage over conventional systems, but the maximum a posteriori (MAP) structure is suboptimal, while the IAMMSE decoder makes limited use of the redundancy. Alternatively, combining aspects of both approaches, we propose a sequence-based approximate MMSE (SAMMSE) decoder. For a Markovian sequence of encoder-produced symbols and a discrete memoryless channel, we approximate the expected distortion at the decoder under the constraint of fixed decoder complexity. For this simplified cost, the optimal decoder computes expected values based on a discrete hidden Markov model, using the wellknown forward/backward (F/B) algorithm. Performance gains for this scheme are demonstrated over previous techniques in quantizing Gauss-Markov sources over a range of noisy channel conditions. Moreover, a constrained delay version is also suggested.

    UR - http://www.scopus.com/inward/record.url?scp=0032001687&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=0032001687&partnerID=8YFLogxK

    U2 - 10.1109/26.659481

    DO - 10.1109/26.659481

    M3 - Article

    VL - 46

    SP - 222

    EP - 231

    JO - IEEE Transactions on Communications

    JF - IEEE Transactions on Communications

    SN - 0096-1965

    IS - 2

    ER -