University of Oulu

N. T. Nguyen, K. Lee and H. DaiIEEE, "Application of Deep Learning to Sphere Decoding for Large MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6787-6803, Oct. 2021, doi: 10.1109/TWC.2021.3076527

Application of deep learning to sphere decoding for large MIMO systems

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Author: Nguyen, Nhan Thanh1,2; Lee, Kyungchun1,3; Dai, Huaiyu4
Organizations: 1Seoul National University of Science and Technology, Seoul, Republic of Korea
2Centre for Wireless Communications, University of Oulu, Oulu, Finland
3Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, Republic of Korea
4North Carolina State University, Raleigh, NC, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-12-21


Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided K -best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a 24×24 MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a 32×32 MIMO system with QPSK, the proposed FDL-KSD only requires K=32 to attain the performance of the conventional KSD with K=256, where K is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 20
Issue: 10
Pages: 6787 - 6803
DOI: 10.1109/TWC.2021.3076527
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research was supported, in part, by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A6A1A03032119) and by the NRF grant funded by the Korea government (MSIT) (NRF-2019R1F1A1061934).
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