降噪自编码器辅助的下行MIMO-SCMA编解码方法

doi:10.19665/j.issn1001-2400.2022.03.009

Abstract

Abstract:

Aiming to improve the bit error rate (BER) performance of sparse code multiple access (SCMA)systems in multi-antenna applications,deep learning is introduced in a MIMO-SCMA system and a denoising autoencoder-aided Codec method (DAE-MIMO-SCMA) is proposed.Multiple deep neural network (DNN) units are used by the transmitter to construct the MIMO-SCMA encoder.The codebook of each user on different transmitting antennas is obtained through neural network (NN) learning.Moreover,the noise layer is used at the transmitter so that the output of the encoder is more robust.At the receiver is designed a fully connected DNN as a decoder,which combines multi-antenna detection and multi-user detection to obtains the original data of all users at one time.An end-to-end training method is used to train the Codec,optimizing the structure and parameters of the NN,which improves the convergent rate.Experimental results show that the proposed Codec method can lower the BER of the MIMO-SCMA system and reduce the detection time at the receiver.

Key words: multiple input multiple output, sparse code multiple access, denoising autoencoder, deep neural network

CLC Number:

TN914.5

JIANG Fang,HUANG Xing,HU Mengyu,WANG Yi,XU Yaohua,HU Yanjun. Denoising autoencoder-aided downlink MIMO-SCMA codec method[J].Journal of Xidian University, 2022, 49(3): 74-82.

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[1]	LARSSONE G, TUFVESSON F, EDFORS O, et al. Massive MIMO for Next Generation Wireless Systems[J]. IEEE Communications Magazine, 2014, 52(2):186-195.
[2]	SAITO Y, KISHIYAMA Y, BENJEBBOUR A, et al. Non-Orthogonal Multiple Access (NOMA) for Cellular Future Radio Access[C]// 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).Piscataway:IEEE, 2013:1-5.
[3]	NIKOPOUR H, BALIGH H. Sparse Code Multiple Access[C]// 2013 IEEE 24th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications (PIMRC).Piscataway:IEEE, 2013:332-336.
[4]	HAN S, GUO C, MENG W, et al. The Uplink and Downlink Design of MIMO-SCMA System[C]// International Wireless Communications and Mobile Computing Conference.Piscataway:IEEE, 2016:56-60.
[5]	KURNIAWAN D, ARIFIANTO M S, KURNIAWAN A. Low Complexity MIMO-SCMA Detector[C]// 2019 IEEE 5th International Conference on Wireless and Telematics (ICWT).Piscataway:IEEE, 2019:1-5.
[6]	DU Y, DONG B, CHEN Z, et al. Joint Sparse Graph-Detector Design for Downlink MIMO-SCMA Systems[J]. IEEE Wireless Communication Letters, 2017, 6(1):14-17.
[7]	MHEICH Z, HEMADEH I A, LIU Z, et al. Low-Complexity Expectation Propagation Detection for Uplink MIMO-SCMA Systems[C]// 2020 International Conference on UK-China Emerging Technologies (UCET).Piscataway:IEEE, 2020:1-4.
[8]	WANG P, LIU L, ZHOU S, et al. Near-Optimal MIMO-SCMA Uplink Detection with Low-Complexity Expectation Propagation[J]. IEEE Transactions on Wireless Communications, 2020, 19(2):1025-1037. doi: 10.1109/TWC.2019.2950314
[9]	潘志鹏, 雷菁, 文磊, 等. 高斯近似消息传播SM-SCMA多用户检测算法[J]. 西安电子科技大学学报, 2020, 47(6):37-44.
	PANG Zhipeng, LEI Jing, WEN Lei, et al. Gaussian-Approximated Message Passing Algorithm for the SM-SCMA System[J]. Journal of Xidian University, 2020, 47(6):37-44.
[10]	DAI J, NIU K, LIN J. Iterative Gaussian-Approximated Message Passing Receiver for MIMO-SCMA System[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(3):753-765. doi: 10.1109/JSTSP.2019.2898323
[11]	YAN C, KANG G X, ZHANGN. A Dimension Distance-Based SCMA Codebook Design[J]. IEEE Access, 2017, 5:5471-5479. doi: 10.1109/ACCESS.2017.2685618
[12]	付伟. 基于遗传算法的下行MIMO-SCMA系统码本设计[D]. 西安: 西安电子科技大学, 2018.
[13]	PAN Z P,LIUW, LEI J, et al. Multi-Dimensional Space-Time Block Coding Aided Downlink MIMO-SCMA[J]. IEEE Transactions on Vehicular Technology, 2019, 68(7):6657-6669. doi: 10.1109/TVT.2019.2915351
[14]	LU C, XU W, SHEN H, et al. An Enhanced SCMA Detector Enabled by Deep Neural Network[C]// 2018 IEEE/CIC International Conference on Communications in China (ICCC).Piscataway:IEEE, 2018:1-5.
[15]	ABIDI I, HIZEM M, AHRIZ I, et al. Convolutional Neural Networks for Blind Decoding in Sparse Code Multiple Access[C]// 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).Piscataway:IEEE, 2019:24-28.
[16]	SAMUEL N, DISKIN T, WIESELA. Deep MIMO Detection[C]// 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).Piscataway:IEEE, 2017:1-5.
[17]	WANG T Q, WEN C K, WANG H Q, et al. Deep Learning for Wireless Physical Layer:Opportunities and Challenges[J]. China Communication, 2017, 14(11):92-111. doi: 10.1109/CC.2017.8233654
[18]	XU W H, ZHONG Z W, BEERY Y, et al. Joint Neural Network Equalizer and Decoder[C]// 2018 15th International Symposium on Wireless Communication Systems (ISWCS).Piscataway:IEEE, 2018:1-6.
[19]	O’SHEA T, HOYDIS J. An Introduction to Deep Learning for the Physical Layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4):563-575. doi: 10.1109/TCCN.2017.2758370
[20]	KIM M, KIM N I, LEE W, et al. Deep Learning-Aided SCMA[J]. IEEE Communications Letters, 2018, 22(4):720-723. doi: 10.1109/LCOMM.2018.2792019
[21]	HAN Y, WANG Z, GUO Q, et al. Deep Learning-Based Detection for Moderate-Density Code Multiple Access in IoT Networks[J]. IEEE Communications Letters, 2020, 24(1):122-125. doi: 10.1109/LCOMM.2019.2952392
[22]	LIN J Z, FENG S Z, ZHANG Y, et al. A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access[J]. Neurocomputing, 2020, 382:52-63. doi: 10.1016/j.neucom.2019.11.066

Denoising autoencoder-aided downlink MIMO-SCMA codec method

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