SCMA Decoding via Deep Learning

被引：25

作者：

Wei, Chia-Po ^{[1
]}

Yang, Han ^{[2
]}

Li, Chih-Peng ^{[2
]}

Chen, Yen-Ming ^{[2
]}

机构：

[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 804, Taiwan

[2] Natl Sun Yat Sen Univ, Inst Commun Engn, Kaohsiung 80424, Taiwan

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2021年 / 10卷 / 04期

关键词：

Neural networks; Bit error rate; Training data; Receivers; Fading channels; Downlink; NOMA; Sparse code multiple access (SCMA); deep neural network (DNN); bit error rate (BER); deep learning;

D O I：

10.1109/LWC.2020.3048068

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sparse code multiple access (SCMA) has become a highly competitive technology for future cellular systems. For the receiver of the SCMA system, besides the traditional maximum likelihood and message passing algorithm solutions, a deep neural network (DNN) method that causes whirlwinds in image recognition can reduce the computational complexity of the decoder. We expect low complexity while maintaining a satisfactory bit error rate (BER) performance. As shown in our simulations, our proposed solution has better BER performance and lower computational complexity than other previously studied DNN solutions.

引用

页码：878 / 881

页数：4

共 19 条

[1]

Altera Innovate Asia, 1 5G ALG INN COMP SC

[2]

[Anonymous], 2015, P ICLR

[3]

Choi JI, 2010, MOBICOM 10 & MOBIHOC 10: PROCEEDINGS OF THE 16TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING AND THE 11TH ACM INTERNATIONAL SYMPOSIUM ON MOBILE AD HOC NETWORKING AND COMPUTING, P1

[4] Low-Complexity Detector in Sparse Code Multiple Access Systems [J].

Du, Yang ;

Dong, Binhong ;

Chen, Zhi ;

Fang, Jun ;

Gao, Pengyu ;

Liu, Zeyuan .

IEEE COMMUNICATIONS LETTERS, 2016, 20 (09) :1812-1815

[5] OFDM Versus Filter Bank Multicarrier [J].

Farhang-Boroujeny, Behrouz .

IEEE SIGNAL PROCESSING MAGAZINE, 2011, 28 (03) :92-112

[6]

Garland M, 2017, ARXIV171202029

[7]

Glorot X., 2010, Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, P249

[8]

Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1

[9] Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning [J].

Huang, Chongwen ;

Mo, Ronghong ;

Yuen, Chau .

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2020, 38 (08) :1839-1850

[10] Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems [J].

Huang, Chongwen ;

Liu, Lei ;

Yuen, Chau ;

Sun, Sumei .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (01) :245-259

← 1 2 →