About author:WANG Anyi, born in 1968, Ph. D. , professor. His research interests include broadband digital mobile communication, intelligent information processing, intelligent coal mine. ZHANG Heng, born in 1995, M. S. candidate. His research interests include Multi-Input Multi-Output (MIMO) detection and decoding.
Supported by:
National Natural Science Foundation of China Joint Fund(U19B2015)
Anyi WANG, Heng ZHANG. Multi-input multi-output intelligent receiver model based on multi-label classification algorithm[J]. Journal of Computer Applications, 2022, 42(10): 3124-3129.
GRADONI G, ANTONSEN T M, OTT E. Influence of multi-path fading on MIMO/OAM communications[C]// Proceedings of the 2019 International Conference on Electromagnetics in Advanced Applications. Piscataway: IEEE, 2019:1230-1230. 10.1109/iceaa.2019.8879262
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XU G P, XIAO T, TAO T, et al. Research on intelligent optimization of massive MIMO coverage based on 5G MR[C]// Proceedings of the 2020 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking. Piscataway: IEEE, 2020:1455-1459. 10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00218
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LeCUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015: 521(7553): 436-444. 10.1038/nature14539
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SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015: 61:85-117. 10.1016/j.neunet.2014.09.003
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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. 10.1109/tccn.2017.2758370
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WANG T Q, WEN C K, WANG H Q, et al. Deep learning for wireless physical layer: opportunities and challenges[J]. China Communications, 2017, 14(11):92-111. 10.1109/cc.2017.8233654
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KARRA K, KUZDEBA S, PETERSEN J. Modulation recognition using hierarchical deep neural networks[C]// Proceedings of the 2017 IEEE International Symposium on Dynamic Spectrum Access Networks. Piscataway: IEEE, 2017: 1-3. 10.1109/dyspan.2017.7920746
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YE H, LI G Y, JUANG B H. Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2018, 7(1):114-117. 10.1109/lwc.2017.2757490
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NACHMANI E, BE’ERY Y, BURSHTEIN D. Learning to decode linear codes using deep learning[C]// Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing. Piscataway: IEEE, 2016:341-346. 10.1109/allerton.2016.7852251
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BAI Q B, WANG J T, ZHANG Y, et al. Deep learning based channel estimation algorithm over time selective fading channels[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(1):125-134. 10.1109/tccn.2019.2943455
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ZHANG L, YANG L L. Machine learning for joint channel equalization and signal detection[M]// LUO F L. Machine Learning for Future Wireless Communications. New York: John Wiley & Sons, Inc., 2020:213-241. 10.1002/9781119562306.ch12
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YE H, LI G Y. Initial results on deep learning for joint channel equalization and decoding[C]// Proceedings of the 2017 IEEE 86th Vehicular Technology Conference. Piscataway: IEEE, 2017: 1-5. 10.1109/vtcfall.2017.8288419
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SPENCER Q H, SWINDLEHURST A L, HAARDT M. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels[J]. IEEE Transactions on Signal Processing, 2004, 52(2):461-471. 10.1109/tsp.2003.821107
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刘景利. MIMO空间复用系统若干关键技术研究[D]. 北京:北京交通大学, 2009:7-26.
LIU J L. Studies on several key technologies in MIMO spatial multiplexing system[D]. Beijing: Beijing Jiaotong University, 2009:7-26.
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ZHENG S L, CHEN S C, YANG X N. DeepReceiver: a deep learning-based intelligent receiver for wireless communications in the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(1):5-20. 10.1109/tccn.2020.3018736
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CHANG W C, YU H F, ZHONG K, et al. Taming pretrained transformers for extreme multi-label text classification[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 3163-3171. 10.1145/3394486.3403368
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ZHANG M L, FANG J P. Partial multi-label learning via credible label elicitation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10):3587-3599. 10.1109/tpami.2020.2985210
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DURAND T, MEHRASA N, MORI G. Learning a deep convnet for multi-label classification with partial labels[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 647-657. 10.1109/cvpr.2019.00074