Deep Learning for Joint MIMO Detection and Channel Decoding

被引:0
作者
Wang, Taotao [1 ]
Zhang, Lihao [2 ]
Liew, Soung Chang [2 ]
机构
[1] Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518060, Peoples R China
[2] Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Peoples R China
来源
2019 IEEE 30TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) | 2019年
基金
中国国家自然科学基金;
关键词
CAPACITY; SEARCH; COMPLEXITY; CODES;
D O I
10.20944/preprints201812.0253.v1
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.
引用
收藏
页码:1292 / 1298
页数:7
相关论文
共 30 条
[1]   Closest point search in lattices [J].
Agrell, E ;
Eriksson, T ;
Vardy, A ;
Zeger, K .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2002, 48 (08) :2201-2214
[2]  
[Anonymous], 2015, Nature, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
[3]   Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels [J].
Arikan, Erdal .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2009, 55 (07) :3051-3073
[4]   Fixing the complexity of the sphere decoder for MIMO detection [J].
Barbero, Luis G. ;
Thompson, John S. .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7 (06) :2131-2142
[5]   IEEE 802.11ac: From Channelization to Multi-User MIMO [J].
Bejarano, Oscar ;
Knightly, Edward W. ;
Park, Minyoung .
IEEE COMMUNICATIONS MAGAZINE, 2013, 51 (10) :84-90
[6]   Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals [J].
Biguesh, M ;
Gershman, AB .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (03) :884-893
[7]  
Cammerer S., 2017, P IEEE GLOBAL COMMUN, P1
[8]  
Ghosh A., 2011, Essentials Of Lte And Lte-A
[9]   Capacity limits of MIMO channels [J].
Goldsmith, A ;
Jafar, SA ;
Jindal, N ;
Vishwanath, S .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2003, 21 (05) :684-702
[10]   Remote monitoring technology and servitisedstrategies - factors characterising the organisational application [J].
Grubic, Tonci ;
Jennions, Ian .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (06) :2133-2149