智能无线通信技术研究概况

doi:10.11959/j.issn.1000-436x.2020145

Abstract

Abstract:

In recent years,artificial intelligence (AI) has been applied to wireless communications,in order to address the challenges introduced by data explosion and Internet of everything.Firstly,three core technologies of AI were introduced,including deep learning,deep reinforcement learning,and federated learning.Then,an overview of their applications on wireless communications was provided,from the aspects of wireless transmission,spectrum management,resource allocation,network access,network and system optimization.Based on the overview,the principle,applicability,design methodology,pros and cons on applying AI technologies to solve wireless communication problems were analyzed and summarized.Regarding the existed limitations,the future development trends and research directions on intelligent wireless communication technologies were pointed out,to hopefully provide useful help and reference for the future research in this field.

Key words: artificial intelligence, wireless communication, deep learning, deep reinforcement learning, federated learning

CLC Number:

TN929.5

Yingchang LIANG,Junjie TAN,Dusit Niyato. Overview on intelligent wireless communication technology[J]. Journal on Communications, 2020, 41(7): 1-17.

Figures/Tables 2

References 86

[1]	CISCO. Cisco visual networking index:global mobile data traffic forecast update,2017–2022[R]. Cisco Public Information, 2017.
[2]	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015,521(7553): 436-444.
[3]	MNIH V , KAVUKCUOGLU K , SILVER D ,et al. Human-level control through deep reinforcement learning[J]. Nature, 2015,518(7540): 529-533.
[4]	YANG Q , LIU Y , CHEN T ,et al. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2019,10(2): 1-19.
[5]	LECUN Y , BOTTOU L , BENGIO Y ,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11): 2278-2324.
[6]	YOUNG T , HAZARIKA D , PORIA S ,et al. Recent trends in deep learning based natural language processing[J]. IEEE Computational Intelligence Magazine, 2018,13(3): 55-75.
[7]	AMARJYOTI S . Deep reinforcement learning for robotic manipulation-the state of the art[J]. arXiv Preprint,arXiv:1701.08878, 2017
[8]	MCMAHAN H B , MOORE E , RAMAGE D ,et al. Communication-efficient learning of deep networks from decentralized data[J]. arXiv Preprint,arXiv:1602.05629, 2016
[9]	BISHOP C M . Pattern recognition and machine learning[M]. Berlin: SpringerPress, 2006.
[10]	CYBENKO G . Approximation by superpositions of a sigmoidal function[J]. Mathematics of Control,Signals and Systems, 1989,2(4): 303-314.
[11]	DAYAN P , ABBOTT L F . Theoretical neuroscience:computational and mathematical modeling of neural systems[M]. Cambridge: MIT PressPress, 2001.
[12]	MIKOLOV T , KARAFIáT M , BURGET L ,et al. Recurrent neural network based language model[C]// Eleventh Annual Conference of the International Speech Communication Association. Trier:DBLP, 2010: 1045-1048.
[13]	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural computation, 1997,9(8): 1735-1780.
[14]	LIU W , WANG Z , LIU X ,et al. A survey of deep neural network architectures and their applications[J]. Neurocomputing, 2017,234: 11-26.
[15]	LUONG N C , HOANG D T , GONG S ,et al. Applications of deep reinforcement learning in communications and networking:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(4): 3133-3174.
[16]	SUTTON R S , BARTO A G . Introduction to reinforcement learning[M]. Cambridge: MIT pressPress, 1998.
[17]	VAN H H , GUEZ A , SILVER D . Deep reinforcement learning with double q-learning[C]// Thirtieth AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2016: 2094-2100.
[18]	WANG Z , SCHAUL T , HESSEL M ,et al. Dueling network architectures for deep reinforcement learning[J]. arXiv Preprint,arXiv:1511.06581, 2015
[19]	LILLICRAP T P , HUNT J J , PRITZEL A ,et al. Continuous control with deep reinforcement learning[J]. arXiv Preprint,arXiv:1509.02971, 2015
[20]	SCHULMAN J , WOLSKI F , DHARIWAL P ,et al. Proximal policy optimization algorithms[J]. arXiv Preprint,arXiv:1707.06347, 2017
[21]	MNIH V , BADIA A P , MIRZA M ,et al. Asynchronous methods for deep reinforcement learning[C]// International Conference on Machine Learning. New York:ACM Press, 2016: 1928-1937.
[22]	NEUMANN D , WIESE T , UTSCHICK W . Learning the MMSE channel estimator[J]. IEEE Transactions on Signal Processing, 2018,66(11): 2905-2917.
[23]	BALEVI E , DOSHI A , ANDREWS J G . Massive MIMO channel estimation with an untrained deep neural network[J]. IEEE Transactions on Wireless Communications, 2020,19(3): 2079-2090.
[24]	O'SHEA T , KARRA K , CLANCY T C . Learning approximate neural estimators for wireless channel state information[C]// 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). Piscataway:IEEE Press, 2017: 1-7.
[25]	CHUN C J , KANG J M , KIM I M . Deep learning-based joint pilot design and channel estimation for multiuser MIMO channels[J]. IEEE Communications Letters, 2019,23(11): 1999-2003.
[26]	QING C , CAI B , YANG Q ,et al. ELM-based superimposed CSI feedback for FDD Massive MIMO system[J]. IEEE Access, 2020,8: 53408-53418.
[27]	ARNOLD M , D?RNER S , CAMMERER S ,et al. Enabling FDD massive MIMO through deep learning-based channel prediction[J]. arXiv Preprint,arXiv:1901.03664, 2019
[28]	YANG Y , GAO F , LI G Y ,et al. Deep learning-based downlink channel prediction for FDD massive MIMO system[J]. IEEE Communications Letters, 2019,23(11): 1994-1998.
[29]	LIANG Y C , CHIN F P S . Downlink channel covariance matrix (DCCM) estimation and its applications in wireless DS-CDMA systems[J]. IEEE Journal on Selected Areas in Communications, 2001,19(2): 222-232.
[30]	WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5): 748-751.
[31]	WANG T , WEN C K , JIN S ,et al. Deep learning-based CSI feedback approach for time-varying massive MIMO channels[J]. IEEE Wireless Communications Letters, 2018,8(2): 416-419.
[32]	HE H , WEN C K , JIN S ,et al. A model-driven deep learning network for MIMO detection[C]// 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Piscataway:IEEE Press, 2018: 584-588.
[33]	TAN X , ZHONG Z , ZHANG Z ,et al. Low-Complexity message passing MIMO detection algorithm with deep neural network[C]// 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Piscataway:IEEE Press, 2018: 559-563.
[34]	WEI Y , ZHAO M M , ZHAO M ,et al. Learned conjugate gradient descent network for massive MIMO detection[J]. arXiv Preprint,arXiv:1906.03814, 2019
[35]	SAMUEL N , DISKIN T , WIESEL A . Learning to detect[J]. IEEE Transactions on Signal Processing, 2019,67(10): 2554-2564.
[36]	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, 2017,7(1): 114-117.
[37]	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.
[38]	XUE S , MA Y , YI N ,et al. Unsupervised deep learning for MU-SIMO joint transmitter and noncoherent receiver design[J]. IEEE Wireless Communications Letters, 2018,8(1): 177-180.
[39]	BALEVI E , ANDREWS J G . One-bit OFDM receivers via deep learning[J]. IEEE Transactions on Communications, 2019,67(6): 4326-4336.
[40]	LIANG Y C . Dynamic spectrum management:from cognitive radio to blockchain and artificial intelligence[M]. Berlin: SpringerPress, 2020.
[41]	LIU C , WANG J , LIU X ,et al. Deep CM-CNN for spectrum sensing in cognitive radio[J]. IEEE Journal on Selected Areas in Communications, 2019,37(10): 2306-2321.
[42]	XIE J , LIU C , LIANG Y C ,et al. Activity pattern aware spectrum sensing:a cnn-based deep learning approach[J]. IEEE Communications Letters, 2019,23(6): 1025-1028.
[43]	LEES W M , WUNDERLICH A , JEAVONS P J ,et al. Deep learning classification of 3.5-GHz band spectrograms with applications to spectrum sensing[J]. IEEE Transactions on Cognitive Communications and Networking, 2019,5(2): 224-236.
[44]	LEE W , KIM M , CHO D H . Deep cooperative sensing:cooperative spectrum sensing based on convolutional neural networks[J]. IEEE Transactions on Vehicular Technology, 2019,68(3): 3005-3009.
[45]	YU L , WANG Q , GUO Y ,et al. Spectrum availability prediction in cognitive aerospace communications:a deep learning perspective[C]// 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). Piscataway:IEEE Press, 2017: 1-4.
[46]	YU L , CHEN J , DING G ,et al. Spectrum prediction based on Taguchi method in deep learning with long short-term memory[J]. IEEE Access, 2018,6: 45923-45933.
[47]	YU Y , WANG T , LIEW S C . Deep-reinforcement learning multiple access for heterogeneous wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2019,37(6): 1277-1290.
[48]	TAN J , ZHANG L , LIANG Y C ,et al. Intelligent sharing for LTE and Wi-Fi systems in unlicensed bands:a deep reinforcement learning approach[J]. IEEE Transactions on Communications, 2020,68(5): 2793-2808.
[49]	WANG S , LIU H , GOMES P H ,et al. Deep reinforcement learning for dynamic multichannel access in wireless networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(2): 257-265.
[50]	NAPARSTEK O , COHEN K . Deep multi-user reinforcement learning for distributed dynamic spectrum access[J]. IEEE Transactions on Wireless Communications, 2018,18(1): 310-323.
[51]	LIU X , XU Y , JIA L ,et al. Anti-jamming communications using spectrum waterfall:a deep reinforcement learning approach[J]. IEEE Communications Letters, 2018,22(5): 998-1001.
[52]	XIE W , HU S , YU C ,et al. Deep learning in digital modulation recognition using high order cumulants[J]. IEEE Access, 2019,7: 63760-63766.
[53]	LUO B , PENG Q , COSMAN P C ,et al. Robustness of deep modulation recognition under AWGN and Rician fading[C]// 2018 52nd Asilomar Conference on Signals,Systems,and Computers. Piscataway:IEEE Press, 2018: 447-450.
[54]	LIN Y , TU Y , DOU Z ,et al. The application of deep learning in communication signal modulation recognition[C]// 2017 IEEE/CIC International Conference on Communications in China (ICCC). Piscataway:IEEE Press, 2017: 1-5.
[55]	WANG Y , LIU M , YANG J ,et al. Data-driven deep learning for automatic modulation recognition in cognitive radios[J]. IEEE Transactions on Vehicular Technology, 2019,68(4): 4074-4077.
[56]	KARRA K , KUZDEBA S , PETERSEN J . Modulation recognition using hierarchical deep neural networks[C]// 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). Piscataway:IEEE Press, 2017: 1-3.
[57]	ZENG Y , ZHANG M , HAN F ,et al. Spectrum analysis and convolutional neural network for automatic modulation recognition[J]. IEEE Wireless Communications Letters, 2019,8(3): 929-932.
[58]	HONG D , ZHANG Z , XU X . Automatic modulation classification using recurrent neural networks[C]// 2017 3rd IEEE International Conference on Computer and Communications (ICCC). Piscataway:IEEE Press, 2017: 695-700.
[59]	ZHANG D , DING W , ZHANG B ,et al. Automatic modulation classification based on deep learning for unmanned aerial vehicles[J]. Sensors, 2018,18(3):924.
[60]	ZHANG M , ZENG Y , HAN Z ,et al. Automatic modulation recognition using deep learning architectures[C]// 2018 IEEE 19th Internation al Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway:IEEE Press, 2018: 1-5.
[61]	HU S , PEI Y , LIANG P P ,et al. Deep neural network for robust modulation classification under uncertain noise conditions[J]. IEEE Transactions on Vehicular Technology, 2020,69(1): 564-577.
[62]	LI X , FANG J , CHENG W ,et al. Intelligent power control for spectrum sharing in cognitive radios:a deep reinforcement learning approach[J]. IEEE Access, 2018,6: 25463-25473.
[63]	NASIR Y S , GUO D . Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2019,37(10): 2239-2250.
[64]	TAN J , ZHANG L , LIANG Y C . Deep reinforcement learning for channel selection and power control in D2D networks[C]// 2019 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2019: 1-6.
[65]	YE H , LI G Y , JUANG B H F . Deep reinforcement learning based resource allocation for V2V communications[J]. IEEE Transactions on Vehicular Technology, 2019,68(4): 3163-3173.
[66]	QIAN Y , HU L , CHEN J ,et al. Privacy-aware service placement for mobile edge computing via federated learning[J]. Information Sciences, 2019,505: 562-570.
[67]	REN J , WANG H , HOU T ,et al. Federated learning-based computation offloading optimization in edge computing-supported Internet of things[J]. IEEE Access, 2019,7: 69194-69201.
[68]	XU Y , XU W , WANG Z ,et al. Load balancing for ultradense networks:a deep reinforcement learning-based approach[J]. IEEE Internet of Things Journal, 2019,6(6): 9399-9412.
[69]	ZHAO N , LIANG Y C , NIYATO D ,et al. Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks[J]. IEEE Transactions on Wireless Communications, 2019,18(11): 5141-5152.
[70]	ZHANG C , LIU Z , GU B ,et al. A deep reinforcement learning based approach for cost-and energy-aware multiflow mobile data offloading[J]. IEICE Transactions on Communications, 2018,7: 1625-1634.
[71]	FENG K , WANG Q , LI X ,et al. Deep reinforcement learning based intelligent reflecting surface optimization for MISO communication systems[J]. IEEE Wireless Communications Letters, 2020,9(5): 745-749.
[72]	LIU J , KRISHNAMACHARI B , ZHOU S ,et al. DeepNap:data-driven base station sleeping operations through deep reinforcement learning[J]. IEEE Internet of Things Journal, 2018,5(6): 4273-4282.
[73]	JUNHONG Y E , ZHANG Y J . DRAG:deep reinforcement learning based base station activation in heterogeneous networks[J]. IEEE Transactions on Mobile Computing, 2019,doi:10.1109/TMC.2019.2922602.
[74]	WU J , YU P , FENG L ,et al. 3D aerial base station position planning based on deep Q-network for capacity enhancement[C]// 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Piscataway:IEEE Press, 2019: 482-487.
[75]	MISMAR F B , EVANS B L . Deep Q-learning for self-organizing networks fault management and radio performance improvement[C]// 2018 52nd Asilomar Conference on Signals,Systems,and Computers. Piscataway:IEEE Press, 2018: 1457-1461.
[76]	NGUYEN T D , MARCHAL S , MIETTINEN M ,et al. D?oT:a federated self-learning anomaly detection system for IoT[C]// 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2019: 756-767.
[77]	QING C , YU W , CAI B ,et al. ELM-based frame synchronization in burst-mode communication systems with nonlinear distortion[J]. IEEE Wireless Communications Letters, 2020,9(6): 915-919.
[78]	HUTTER F , KOTTHOFF L , VANSCHOREN J . Automated machine learning[M]. Berlin: SpringerPress, 2019.
[79]	NICHOL A , SCHULMAN J . Reptile:a scalable meta-learning algorithm[J]. arXiv Preprint,arXiv:1803.02999, 2018
[80]	SILVER D , SCHRITTWIESER J , SIMONYAN K ,et al. Mastering the game of go without human knowledge[J]. Nature, 2017,550(7676): 354-359.
[81]	王元卓, 林闯, 程学旗 ,等. 基于随机博弈模型的网络攻防量化分析方法[J]. 计算机学报, 2010,33(9): 1748-1762.
	WANG Y Z , LIN C , CHENG X Q ,et al. Analysis for network attack-defense based on stochastic game model[J]. Chinese Journal of Computers, 2010,33(9): 1748-1762.
[82]	LIN Y , HAN S , MAO H ,et al. Deep gradient compression:reducing the communication bandwidth for distributed training[J]. arXiv Preprint,arXiv:1712.01887, 2017
[83]	KONE?NY J , MCMAHAN H B , YU F X ,et al. Federated learning:strategies for improving communication efficiency[J]. arXiv Preprint,arXiv:1610.05492, 2016
[84]	HARDY S , HENECKA W , IVEY-LAW H ,et al. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption[J]. arXiv Preprint,arXiv:1711.10677, 2017
[85]	GEYER R C , KLEIN T , NABI M . Differentially private federated learning:a client level perspective[J]. arXiv Preprint,arXiv:1712.07557, 2017
[86]	LI L , XU W , CHEN T ,et al. RSA:Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2019: 1544-1551.

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