IEICE TRANSACTIONS on Fundamentals
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User Identification and Channel Estimation by Iterative DNN-Based Decoder on Multiple-Access Fading Channel
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Summary :
In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.3 pp.417-424
- Publication Date
- 2022/03/01
- Publicized
- 2021/09/01
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021TAP0008
- Type of Manuscript
- Special Section PAPER (Special Section on Information Theory and Its Applications)
- Category
- Communication Theory and Signals
Authors
Lantian WEI
Gifu University
Shan LU
Gifu University
Hiroshi KAMABE
Gifu University
Jun CHENG
Doshisha University
Keyword
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Lantian WEI, Shan LU, Hiroshi KAMABE, Jun CHENG, "User Identification and Channel Estimation by Iterative DNN-Based Decoder on Multiple-Access Fading Channel" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 3, pp. 417-424, March 2022, doi: 10.1587/transfun.2021TAP0008.
Abstract: In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021TAP0008/_p
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@ARTICLE{e105-a_3_417,
author={Lantian WEI, Shan LU, Hiroshi KAMABE, Jun CHENG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={User Identification and Channel Estimation by Iterative DNN-Based Decoder on Multiple-Access Fading Channel},
year={2022},
volume={E105-A},
number={3},
pages={417-424},
abstract={In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.},
keywords={},
doi={10.1587/transfun.2021TAP0008},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - User Identification and Channel Estimation by Iterative DNN-Based Decoder on Multiple-Access Fading Channel
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 417
EP - 424
AU - Lantian WEI
AU - Shan LU
AU - Hiroshi KAMABE
AU - Jun CHENG
PY - 2022
DO - 10.1587/transfun.2021TAP0008
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2022
AB - In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.
ER -
English
