Deep Learning-Aided SCMA

Minhoe Kim; Nam I. Kim; Woongsup Lee; Dong Ho Cho

doi:10.1109/LCOMM.2018.2792019

Deep Learning-Aided SCMA

Minhoe Kim, Nam I. Kim, Woongsup Lee, Dong Ho Cho

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

Abstract

Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.

Original language	English
Pages (from-to)	720-723
Number of pages	4
Journal	IEEE Communications Letters
Volume	22
Issue number	4
DOIs	https://doi.org/10.1109/LCOMM.2018.2792019
Publication status	Published - 2018 Apr

Bibliographical note

Funding Information:
Manuscript received December 10, 2017; revised January 5, 2018; accepted January 5, 2018. Date of publication January 11, 2018; date of current version April 7, 2018. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057529) and supported by ‘The Cross-Ministry Giga KOREA Project’ Grant funded by the Korea Government(MSIT) (No.GK17N0100, 5G Mobile Communication System Development based on mmWave). The associate editor coordinating the review of this letter and approving it for publication was Y. Liu. (Corresponding author: Woongsup Lee.) M. Kim, N.-I Kim, and D.-H. Cho are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.

Publisher Copyright:
© 1997-2012 IEEE.

All Science Journal Classification (ASJC) codes

Modelling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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title = "Deep Learning-Aided SCMA",

abstract = "Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.",

author = "Minhoe Kim and Kim, {Nam I.} and Woongsup Lee and Cho, {Dong Ho}",

note = "Funding Information: Manuscript received December 10, 2017; revised January 5, 2018; accepted January 5, 2018. Date of publication January 11, 2018; date of current version April 7, 2018. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057529) and supported by {\textquoteleft}The Cross-Ministry Giga KOREA Project{\textquoteright} Grant funded by the Korea Government(MSIT) (No.GK17N0100, 5G Mobile Communication System Development based on mmWave). The associate editor coordinating the review of this letter and approving it for publication was Y. Liu. (Corresponding author: Woongsup Lee.) M. Kim, N.-I Kim, and D.-H. Cho are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea. Publisher Copyright: {\textcopyright} 1997-2012 IEEE.",

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Deep Learning-Aided SCMA

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