Adaptive Generative Models for Digital Wireless Channels

Omar S. Salih, Cheng-Xiang Wang, Bo Ai, Raed Mesleh

    Research output: Contribution to journalArticle

    3 Citations (Scopus)
    13 Downloads (Pure)

    Abstract

    Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal-to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.
    Original languageEnglish
    Pages (from-to)5173-5182
    JournalIEEE Transactions on Wireless Communications
    Volume13
    Issue number9
    DOIs
    Publication statusPublished - 1 Sep 2014

    Fingerprint

    Generative Models
    Evolution System
    Error statistics
    Burst
    Wireless Communication
    Markov Model
    Long Term Evolution (LTE)
    Hidden Markov models
    Statistics
    Adaptive Techniques
    Signal to noise ratio
    Term
    Error Rate
    Computer Simulation
    Model
    Industry
    Bit error rate
    Telecommunication links

    Bibliographical note

    “© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be
    obtained for all other uses, in any current or future media, including
    reprinting/republishing this material for advertising or promotional purposes, creating
    new collective works, for resale or redistribution to servers or lists, or reuse of any
    copyrighted component of this work in other works.”

    Keywords

    • Adaptive generative models
    • burst error statistics
    • error models
    • hidden Markov models
    • Markov models

    Cite this

    Adaptive Generative Models for Digital Wireless Channels. / Salih, Omar S.; Wang, Cheng-Xiang; Ai, Bo; Mesleh, Raed.

    In: IEEE Transactions on Wireless Communications, Vol. 13, No. 9, 01.09.2014, p. 5173-5182.

    Research output: Contribution to journalArticle

    Salih, Omar S. ; Wang, Cheng-Xiang ; Ai, Bo ; Mesleh, Raed. / Adaptive Generative Models for Digital Wireless Channels. In: IEEE Transactions on Wireless Communications. 2014 ; Vol. 13, No. 9. pp. 5173-5182.
    @article{0629390f5d7040dfbe039b90b91ec1a1,
    title = "Adaptive Generative Models for Digital Wireless Channels",
    abstract = "Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal-to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.",
    keywords = "Adaptive generative models, burst error statistics, error models, hidden Markov models, Markov models",
    author = "Salih, {Omar S.} and Cheng-Xiang Wang and Bo Ai and Raed Mesleh",
    note = "“{\circledC} 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”",
    year = "2014",
    month = "9",
    day = "1",
    doi = "10.1109/TWC.2014.2325028",
    language = "English",
    volume = "13",
    pages = "5173--5182",
    journal = "IEEE Transactions on Wireless Communications",
    issn = "1536-1276",
    publisher = "Institute of Electrical and Electronics Engineers",
    number = "9",

    }

    TY - JOUR

    T1 - Adaptive Generative Models for Digital Wireless Channels

    AU - Salih, Omar S.

    AU - Wang, Cheng-Xiang

    AU - Ai, Bo

    AU - Mesleh, Raed

    N1 - “© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

    PY - 2014/9/1

    Y1 - 2014/9/1

    N2 - Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal-to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.

    AB - Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal-to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.

    KW - Adaptive generative models

    KW - burst error statistics

    KW - error models

    KW - hidden Markov models

    KW - Markov models

    U2 - 10.1109/TWC.2014.2325028

    DO - 10.1109/TWC.2014.2325028

    M3 - Article

    VL - 13

    SP - 5173

    EP - 5182

    JO - IEEE Transactions on Wireless Communications

    JF - IEEE Transactions on Wireless Communications

    SN - 1536-1276

    IS - 9

    ER -