Signal detection for FTN system under unknown noise model with imperfect CSI

Feng Li; Jiangtao Chen; Jiachen Zhang; Tao Liu

doi:10.1016/j.phycom.2023.102155

Signal detection for FTN system under unknown noise model with imperfect CSI

Feng Li
, Jiangtao Chen
, Jiachen Zhang
, Tao Liu

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

Abstract

The problem of signal detection for faster-than-Nyquist (FTN) signaling system with imperfect channel state information (CSI) is studied. Also, the non-Gaussian noise in real applications is considered. A general model, Gaussian mixture noise (GMN) model, is applied to describe the unknown probability density function (PDF) of the non-Gaussian noise. After that, a framework over variational inference is constructed to simplify the problem through finding an auxiliary PDF with factorization form. Based on the factor graph over the signal, joint response and the parameters of GMN, an iterative solution is obtained. Simulations demonstrate the effectiveness of the proposed approach.

Original language	English
Article number	102155
Journal	Physical Communication
Volume	60
DOIs	https://doi.org/10.1016/j.phycom.2023.102155
State	Published - Oct 2023

Keywords

Data detection
Faster-than-Nyquist
Gaussian mixture model
Message passing
Non-Gaussian noise

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10.1016/j.phycom.2023.102155

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title = "Signal detection for FTN system under unknown noise model with imperfect CSI",

abstract = "The problem of signal detection for faster-than-Nyquist (FTN) signaling system with imperfect channel state information (CSI) is studied. Also, the non-Gaussian noise in real applications is considered. A general model, Gaussian mixture noise (GMN) model, is applied to describe the unknown probability density function (PDF) of the non-Gaussian noise. After that, a framework over variational inference is constructed to simplify the problem through finding an auxiliary PDF with factorization form. Based on the factor graph over the signal, joint response and the parameters of GMN, an iterative solution is obtained. Simulations demonstrate the effectiveness of the proposed approach.",

keywords = "Data detection, Faster-than-Nyquist, Gaussian mixture model, Message passing, Non-Gaussian noise",

author = "Feng Li and Jiangtao Chen and Jiachen Zhang and Tao Liu",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

year = "2023",

month = oct,

doi = "10.1016/j.phycom.2023.102155",

language = "英语",

volume = "60",

journal = "Physical Communication",

issn = "1874-4907",

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TY - JOUR

T1 - Signal detection for FTN system under unknown noise model with imperfect CSI

AU - Li, Feng

AU - Chen, Jiangtao

AU - Zhang, Jiachen

AU - Liu, Tao

PY - 2023/10

Y1 - 2023/10

N2 - The problem of signal detection for faster-than-Nyquist (FTN) signaling system with imperfect channel state information (CSI) is studied. Also, the non-Gaussian noise in real applications is considered. A general model, Gaussian mixture noise (GMN) model, is applied to describe the unknown probability density function (PDF) of the non-Gaussian noise. After that, a framework over variational inference is constructed to simplify the problem through finding an auxiliary PDF with factorization form. Based on the factor graph over the signal, joint response and the parameters of GMN, an iterative solution is obtained. Simulations demonstrate the effectiveness of the proposed approach.

AB - The problem of signal detection for faster-than-Nyquist (FTN) signaling system with imperfect channel state information (CSI) is studied. Also, the non-Gaussian noise in real applications is considered. A general model, Gaussian mixture noise (GMN) model, is applied to describe the unknown probability density function (PDF) of the non-Gaussian noise. After that, a framework over variational inference is constructed to simplify the problem through finding an auxiliary PDF with factorization form. Based on the factor graph over the signal, joint response and the parameters of GMN, an iterative solution is obtained. Simulations demonstrate the effectiveness of the proposed approach.

KW - Data detection

KW - Faster-than-Nyquist

KW - Gaussian mixture model

KW - Message passing

KW - Non-Gaussian noise

UR - https://www.scopus.com/pages/publications/85166937801

U2 - 10.1016/j.phycom.2023.102155

DO - 10.1016/j.phycom.2023.102155

M3 - 文章

AN - SCOPUS:85166937801

SN - 1874-4907

VL - 60

JO - Physical Communication

JF - Physical Communication

M1 - 102155

ER -

Signal detection for FTN system under unknown noise model with imperfect CSI

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Keywords

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