AI for 5G: research directions and paradigms

Xiaohu YOU; Chuan ZHANG; Xiaosi TAN; Shi JIN; Hequan WU

doi:10.1007/s11432-018-9596-5

SCIENCE CHINA Information Sciences

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SCIENCE CHINA Information Sciences, Volume 62 , Issue 2 : 021301(2019) https://doi.org/10.1007/s11432-018-9596-5

AI for 5G: research directions and paradigms

Xiaohu YOU ¹, Chuan ZHANG ^1,*, Xiaosi TAN ¹, Shi JIN ¹, Hequan WU ²

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ReceivedJul 23, 2018
AcceptedSep 12, 2018
PublishedOct 26, 2018

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Abstract

end-to-end joint optimization

5G mobile communication

Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61501116, 61521061).

References

[1] You X H, Pan Z W, Gao X Q, et al. The 5G mobile communication: the development trends and its emerging key techniques (in Chinese). Sci Sin Inform, 2014, 44: 551--563. Google Scholar

[2] Li L, Wang D, Niu X. mmWave communications for 5G: implementation challenges and advances. Sci China Inf Sci, 2018, 61: 021301 CrossRef Google Scholar

[3] Wang C X, Wu S B, Bai L, et al. Recent advances and future challenges for massive MIMO channel measurements and models. Sci China Inf Sci, 2016, 59: 021301. Google Scholar

[4] Zhang J, Tang P, Tian L. 6-100 GHz research progress and challenges from a channel perspective for fifth generation (5G) and future wireless communication. Sci China Inf Sci, 2017, 60: 080301 CrossRef Google Scholar

[5] Tao X, Han Y, Xu X. Recent advances and future challenges for mobile network virtualization. Sci China Inf Sci, 2017, 60: 040301 CrossRef Google Scholar

[6] Way forward on the overall 5G-NR eMBB. Workplan RP-170741. 2017. ftp://ftp.3gpp.org/TSG RAN/TSG RAN/TSGR 75/Docs/RP-170741.zip. Google Scholar

[7] Study on new radio access technology: radio access architecture and interfaces (release 14). TR38.801, v14.0. 2017. http://www.3gpp.org/ftp/Specs/archive/38 series/38.801/38801-e00.zip. Google Scholar

[8] ITU-R. Minimum requirements related to technical performance for IMT2020 radio interface(s). Report ITU-R M.2410-0. 2017. https://www.itu.int/pub/R-REP-M.2410-2017. Google Scholar

[9] LTE Enhancements and 5G Normative Work. Release-15. 2018. http://www.3gpp.org/release-15. Google Scholar

[10] You X H, Wang D M, Sheng B. Cooperative distributed antenna systems for mobile communications [Coordinated and Distributed MIMO. IEEE Wireless Commun, 2010, 17: 35-43 CrossRef Google Scholar

[11] Yang W, Wang M, Zhang J. Narrowband Wireless Access for Low-Power Massive Internet of Things: A Bandwidth Perspective. IEEE Wireless Commun, 2017, 24: 138-145 CrossRef Google Scholar

[12] ITU-T. LS/o on the results of the 1st meeting of the ITU-T focus group on ma- chine learning for future networks including 5G (FG ML5G). FG ML5G-0-004. 2018. http://www.3gpp.org/ftp/tsg sa/WG1 Serv/TSGS1 82 Dubrovnik/Docs/S1-181271.zip. Google Scholar

[13] 5G system network data analytics services stage 3. TS 29.520 (CT3). 2018. http://www.etsi.org/deliver/etsi ts/129500 129599/129520/15.00.00 60/ts 129520v150000p.pdf. Google Scholar

[14] Whitley D. A genetic algorithm tutorial. Stat Comput, 1994, 4: 65--85. Google Scholar

[15] Schmidhuber J. Deep learning in neural networks: an overview.. Neural Networks, 2015, 61: 85-117 CrossRef PubMed Google Scholar

[16] Xiao-Hu Yu , Guo-An Chen , Shi-Xin Cheng . Dynamic learning rate optimization of the backpropagation algorithm.. IEEE Trans Neural Netw, 1995, 6: 669-677 CrossRef PubMed Google Scholar

[17] Yu X H. Can backpropagation error surface not have local minima.. IEEE Trans Neural Netw, 1992, 3: 1019-1021 CrossRef PubMed Google Scholar

[18] Yu X H, Chen G A. Efficient Backpropagation Learning Using Optimal Learning Rate and Momentum. Neural Networks, 1997, 10: 517-527 CrossRef Google Scholar

[19] Kaelbling L P, Littman M L, Moore A W. Reinforcement Learning: A Survey. jair, 1996, 4: 237-285 CrossRef Google Scholar

[20] Watkins C J C H, Dayan P. Q-learning. Mach Learn, 1992, 8: 279--292. Google Scholar

[21] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. 2017,. arXiv Google Scholar

[22] Wu J, Gao B B, Wei X S. Resource-constrained deep learning: challenges and practices. Sci Sin-Inf, 2018, 48: 501-510 CrossRef Google Scholar

[23] Zhou Z H. Machine learning: recent progress in China and beyond. China Sci Rev, 2018, 5: 20. Google Scholar

[24] Zhong Y X. Artificial intelligence: Concept, approach and opportunity. Chin Sci Bull, 2017, 62: 2473-2479 CrossRef Google Scholar

[25] Gatherer A. Machine learning modems: how ML will change how we specify and design next generation communication systems. IEEE ComSoc Tech News, 2018. https://www.comsoc.org/ctn/machine-learning-modems-how-ml-will-change-how-we-specify-and-design-next-generation. Google Scholar

[26] Yang C, Xu W H, Zhang Z C, et al. A channel-blind detection for SCMA based on image processing techniques. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2018. 1--5. Google Scholar

[27] Zhang C, Xu W H. Neural networks: efficient implementations and applications. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017. 1029--1032. Google Scholar

[28] Xu W H, You X H, Zhang C. Efficient deep convolutional neural networks accelerator without multiplication and retraining. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. 1--5. Google Scholar

[29] Xu W H, Wang Z F, You X H, et al. Efficient fast convolution architectures for convolutional neural network. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017. 904--907. Google Scholar

[30] Xu W H, Wu Z Z, Ueng Y L, et al. Improved polar decoder based on deep learning. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), 2017. 1--6. Google Scholar

[31] Xu W H, Zhong Z W, Be'ery Y, et al. Joint neural network equalizer and decoder. In: Proceedings of IEEE International Symposium on Wireless Communication Systems (ISWCS), 2018. 1--6. Google Scholar

[32] Xu W H, Be'ery Y, You X H, et al. Polar decoding on sparse graphs with deep learning. In: Proceedings of Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2018. 1--6. Google Scholar

[33] Xu W H, You X H, Zhang C. Using Fermat number transform to accelerate convolutional neural network. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017. 1033--1036. Google Scholar

[34] Gao X, Jiang B, Li X. Statistical Eigenmode Transmission Over Jointly Correlated MIMO Channels. IEEE Trans Inform Theor, 2009, 55: 3735-3750 CrossRef Google Scholar

[35] Wang D, Zhang Y, Wei H. An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications. Sci China Inf Sci, 2016, 59: 081301 CrossRef Google Scholar

[36] Gesbert D, Hanly S, Huang H. Multi-cell MIMO cooperative networks: A new look at interference. IEEE J Sel Areas Commun, 2010, 28: 1380-1408 CrossRef Google Scholar

[37] Jing S S, Yu A L, Liang X, et al. Uniform belief propagation processor for massive MIMO detection and GF (2$^{n}$) LDPC decoding. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2017. 961--964. Google Scholar

[38] Gandhi V S, Maheswaran B. A cross layer design for performance enhancements in LTE-A system. In: Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016. 905--909. Google Scholar

[39] Kuen J, Kong X F, Wang G, et al. DelugeNets: deep networks with efficient and flexible cross-layer information inflows. In: Proceedings of IEEE International Conference on Computer Vision Workshop (ICCVW), 2017. 958--966. Google Scholar

[40] Farsad N, Rao M, Goldsmith A. Deep learning for joint source-channel coding of text. 2018,. arXiv Google Scholar

[41] Xu X, Ding Y, Hu S X. Scaling for edge inference of deep neural networks. Nat Electron, 2018, 1: 216-222 CrossRef Google Scholar

[42] Wang X, Li X, Leung V C M. Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges. IEEE Access, 2015, 3: 1379-1391 CrossRef Google Scholar

[43] Klaine P V, Imran M A, Onireti O. A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks. IEEE Commun Surv Tutorials, 2017, 19: 2392-2431 CrossRef Google Scholar

[44] Pèrez-Romero J, Sallent O, Ferrús R, et al. Knowledge-based 5G radio access network planning and optimization. In: Proceedings of IEEE International Symposium on Wireless Communication Systems (ISWCS), 2016. 359--365. Google Scholar

[45] Gomez-Andrades A, Munoz P, Serrano I. Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques. IEEE Trans Veh Technol, 2016, 65: 2369-2386 CrossRef Google Scholar

[46] Wang J, Guan W, Huang Y. Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework. IEEE J Sel Areas Commun, 2017, 35: 249-264 CrossRef Google Scholar

[47] Bogale T E, Wang X, Le L B. Machine intelligence techniques for next-generation context-aware wireless networks. 2018,. arXiv Google Scholar

[48] Li R, Zhao Z, Zhou X. Intelligent 5G: When Cellular Networks Meet Artificial Intelligence. IEEE Wireless Commun, 2017, 24: 175-183 CrossRef Google Scholar

[49] Zhao Z, Li R, Sun Q, et al. Deep reinforcement learning for network slicing. 2018,. arXiv Google Scholar

[50] Ren Y R, Zhang C, Liu X, et al. Efficient early termination schemes for belief-propagation decoding of polar codes. In: Proceedings of IEEE International Conference on ASIC (ASICON), 2015. 1--4. Google Scholar

[51] Fossorier M P C, Mihaljevic M, Imai H. Reduced complexity iterative decoding of low-density parity check codes based on belief propagation. IEEE Trans Commun, 1999, 47: 673-680 CrossRef Google Scholar

[52] Yang J, Song W, Zhang S. Low-Complexity Belief Propagation Detection for Correlated Large-Scale MIMO Systems. J Sign Process Syst, 2018, 90: 585-599 CrossRef Google Scholar

[53] Liu L, Yuen C, Guan Y L, et al. Gaussian message passing iterative detection for MIMO-NOMA systems with massive access. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), 2016. 1--6. Google Scholar

[54] Yang J M, Zhang C, Zhou H Y, et al. Pipelined belief propagation polar decoders. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2016. 413--416. Google Scholar

[55] Tan X S, Xu W H, Be'ery Y, et al. Improving massive MIMO belief propagation detector with deep neural network. 2018,. arXiv Google Scholar

[56] Liang F, Shen C, Wu F. An Iterative BP-CNN Architecture for Channel Decoding. IEEE J Sel Top Signal Process, 2018, 12: 144-159 CrossRef ADS arXiv Google Scholar

[57] Lv X Z, Wei P, Xiao X C. Automatic identification of digital modulation signals using high order cumulants. Electronic Warfare, 2004, 6: 1. Google Scholar

[58] Wang T, Wen C K, Wang H. Deep learning for wireless physical layer: Opportunities and challenges. China Commun, 2017, 14: 92-111 CrossRef Google Scholar

[59] O'Shea T, Hoydis J. An Introduction to Deep Learning for the Physical Layer. IEEE Trans Cogn Commun Netw, 2017, 3: 563-575 CrossRef Google Scholar

[60] O'Shea T J, Erpek T, Clancy T C. Deep learning based MIMO communications. 2017,. arXiv Google Scholar

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Figure 1
(Color online) Example of supervised learning: learning in deep neural networks.
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Figure 2
(Color online) Example of unsupervised learning: self organizing map.
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Figure 3
(Color online) Example of reinforcement learning: Q-learning.
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Figure 4
(Color online) Automatic root cause analysis workflow [45].
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Figure 5
(Color online) Dynamic resource allocation for multi-cell and multi-user systems.
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Figure 6
(Color online) Systolic array hardware structure for neural networks [28].
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Figure 7
(Color online) Architecture of a receiver including neural network equalizer and decoder [31].
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Figure 8
(Color online) A simple autoencoder for an end-to-end communication system [58].
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Figure 9
(Color online) A general MIMO channel autoencoder architecture [58].

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AI for 5G: research directions and paradigms

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