A survey of 5G network systems: challenges and machine learning approaches
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[1] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[2] Thorsten Wild,et al. Waveform contenders for 5G — OFDM vs. FBMC vs. UFMC , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).
[3] P. Mell,et al. The NIST Definition of Cloud Computing , 2011 .
[4] Hussein Moradi,et al. OFDM Inspired Waveforms for 5G , 2016, IEEE Communications Surveys & Tutorials.
[5] Mehdi Bennis,et al. A transfer learning approach for cache-enabled wireless networks , 2015, 2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).
[6] Yuanyuan Qiao,et al. A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications , 2018, Wirel. Commun. Mob. Comput..
[7] Zhongshan Zhang,et al. Big Data Perspective and Challenges in Next Generation Networks , 2018, Future Internet.
[8] Ali Imran,et al. Fault prediction and reliability analysis in a real cellular network , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).
[9] Zhong Fan,et al. Emerging technologies and research challenges for 5G wireless networks , 2014, IEEE Wireless Communications.
[10] Srinath Srinivasa. Big Data Analytics : First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012. Proceedings , 2012 .
[11] Yuan Yuan,et al. Congested scene classification via efficient unsupervised feature learning and density estimation , 2016, Pattern Recognit..
[12] Ingrid Moerman,et al. A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives , 2018, IEEE Communications Surveys & Tutorials.
[13] Erik G. Larsson,et al. Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.
[14] Raouf Boutaba,et al. Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.
[15] Mohsen Guizani,et al. M2M Communications in 5G: State-of-the-Art Architecture, Recent Advances, and Research Challenges , 2017, IEEE Communications Magazine.
[16] Alagan Anpalagan,et al. Towards the fulfillment of 5G network requirements: technologies and challenges , 2016, Telecommunication Systems.
[17] Tarik Taleb,et al. Machine-type communications: current status and future perspectives toward 5G systems , 2015, IEEE Communications Magazine.
[18] Alireza Sadeghi,et al. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities , 2017, IEEE Journal of Selected Topics in Signal Processing.
[19] Stefano Salsano,et al. 5G PPP Architecture Working Group: View on 5G Architecture (Version 2.0, December 2017) , 2017 .
[20] Lorenza Giupponi,et al. From 4G to 5G: Self-organized Network Management meets Machine Learning , 2017, Comput. Commun..
[21] Shengli Xie,et al. Cognitive machine-to-machine communications: visions and potentials for the smart grid , 2012, IEEE Network.
[22] Junyuan Wang,et al. A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing , 2017, IEEE Network.
[23] Rosdiadee Nordin,et al. Evolution towards fifth generation (5G) wireless networks: Current trends and challenges in the deployment of millimetre wave, massive MIMO, and small cells , 2016, Telecommunication Systems.
[24] Moshe Zukerman,et al. Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.
[25] Kapil Kanwal. Increased energy efficiency in LTE networks through reduced early handover , 2017 .
[26] Tao Zhang,et al. Self-Organized Cell Outage Detection Architecture and Approach for 5G H-CRAN , 2018, Wirel. Commun. Mob. Comput..
[27] Alejandro Zunino,et al. An empirical comparison of botnet detection methods , 2014, Comput. Secur..
[28] Fredrik Tufvesson,et al. 5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice , 2017, IEEE Journal on Selected Areas in Communications.
[29] Shree Krishna Sharma,et al. Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future , 2019, IEEE Access.
[30] S. Z. Iliya,et al. A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System , 2016, IEEE Communications Surveys & Tutorials.
[31] Ilyas Alper Karatepe,et al. Big data caching for networking: moving from cloud to edge , 2016, IEEE Communications Magazine.
[32] Brian L. Evans,et al. Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement , 2017, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.
[33] Dirk Wübben,et al. Cloud technologies for flexible 5G radio access networks , 2014, IEEE Communications Magazine.
[34] Gerd Zimmermann,et al. METIS research advances towards the 5G mobile and wireless system definition , 2015, EURASIP J. Wirel. Commun. Netw..
[35] Kyungwhoon Cheun,et al. Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results , 2014, IEEE Communications Magazine.
[36] Robert W. Heath,et al. Five disruptive technology directions for 5G , 2013, IEEE Communications Magazine.
[37] Liang Gong,et al. An intelligent SDN framework for 5G heterogeneous networks , 2015, IEEE Communications Magazine.
[38] Muhammad Ali Imran,et al. A Multiple Attribute User-Centric Backhaul Provisioning Scheme Using Distributed SON , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).
[39] Laura Pierucci,et al. The quality of experience perspective toward 5G technology , 2015, IEEE Wireless Communications.
[40] Ali Imran,et al. Continuous Time Markov Chain Based Reliability Analysis for Future Cellular Networks , 2014, GLOBECOM 2014.
[41] Nicola Bui,et al. A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques , 2016, IEEE Communications Surveys & Tutorials.
[42] Liu Yang,et al. Easy network: the way to go for 5G , 2015, China Communications.
[43] Ming Xiao,et al. A Survey of Advanced Techniques for Spectrum Sharing in 5G Networks , 2017, IEEE Wireless Communications.
[44] Carolina Fortuna,et al. Trends in the development of communication networks: Cognitive networks , 2009, Comput. Networks.
[45] Seungjoon Lee,et al. Network function virtualization: Challenges and opportunities for innovations , 2015, IEEE Communications Magazine.
[46] Philip Levis,et al. Applications of self-interference cancellation in 5G and beyond , 2014, IEEE Communications Magazine.
[47] Geoffrey Ye Li,et al. Recent advances in energy-efficient networks and their application in 5G systems , 2015, IEEE Wireless Communications.
[48] Arkady B. Zaslavsky,et al. Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.
[49] Ashok Kumar Reddy Chavva,et al. Deep Learning Based Link Failure Mitigation , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).
[50] Yan Chen,et al. Intelligent 5G: When Cellular Networks Meet Artificial Intelligence , 2017, IEEE Wireless Communications.
[51] Sudharman K. Jayaweera,et al. Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).
[52] Gan Zheng,et al. Joint Beamforming Optimization and Power Control for Full-Duplex MIMO Two-Way Relay Channel , 2014, IEEE Transactions on Signal Processing.
[53] Taskin Koçak,et al. Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.
[54] AKHIL GUPTA,et al. A Survey of 5G Network: Architecture and Emerging Technologies , 2015, IEEE Access.
[55] Muhammad Ali Imran,et al. Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.
[56] Xinyu Gu,et al. D2D Power Control Based on Hierarchical Extreme Learning Machine , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).
[57] Dimitri Ktenas,et al. QoS-Driven Scheduling in 5G Radio Access Networks - A Reinforcement Learning Approach , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.
[58] Basem Shihada,et al. Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks , 2018, IEEE Transactions on Mobile Computing.
[59] Josep Mangues-Bafalluy,et al. Big Data Empowered Self Organized Networks , 2014 .
[60] Muhammad Ali Imran,et al. Energy-Efficient SON-Based User-Centric Backhaul Scheme , 2017, 2017 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).
[61] Abdallah Shami,et al. NFV: state of the art, challenges, and implementation in next generation mobile networks (vEPC) , 2014, IEEE Network.
[62] Dharma P. Agrawal,et al. 5G mobile technology: A survey , 2015, ICT Express.
[63] Sudharman K. Jayaweera,et al. Machine learning aided cognitive RAT selection for 5G heterogeneous networks , 2017, 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom).
[64] Rose Qingyang Hu,et al. Enable device-to-device communications underlaying cellular networks: challenges and research aspects , 2014, IEEE Communications Magazine.
[65] Basem Shihada,et al. Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).
[66] Nei Kato,et al. An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach , 2018, IEEE Internet of Things Journal.
[67] Xiaohu You,et al. AI for 5G: research directions and paradigms , 2018, Science China Information Sciences.
[68] Inkyu Lee,et al. Three-Dimensional Beamforming: A new enabling technology for 5G wireless networks , 2014, IEEE Signal Processing Magazine.
[69] Jianhua Zhang. The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model , 2016 .
[70] Subhash Bhalla,et al. Big Data Analytics , 2019, Lecture Notes in Computer Science.
[71] Chaouki T. Abdallah,et al. Power control algorithms in wireless communications , 2002, SPIE Defense + Commercial Sensing.
[72] Navrati Saxena,et al. Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.
[73] Muhammad Ali Imran,et al. A Distributed SON-Based User-Centric Backhaul Provisioning Scheme , 2016, IEEE Access.
[74] Petar Popovski,et al. The METIS 5G System Concept: Meeting the 5G Requirements , 2016, IEEE Communications Magazine.
[75] Raouf Boutaba,et al. Network virtualization: state of the art and research challenges , 2009, IEEE Communications Magazine.
[76] Wei Chen,et al. The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.
[77] Anna Brunstrom,et al. SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey , 2017, IEEE Communications Surveys & Tutorials.
[78] Richard D. Gitlin,et al. A Clustering Algorithm That Maximizes Throughput in 5G Heterogeneous F-RAN Networks , 2018, 2018 IEEE International Conference on Communications (ICC).
[79] Ghada Arfaoui,et al. Security and Resilience in 5G: Current Challenges and Future Directions , 2017, 2017 IEEE Trustcom/BigDataSE/ICESS.
[80] Muhammad Ali Imran,et al. A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.
[81] Zhengang Pan,et al. 5G: rethink mobile communications for 2020+ , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[82] Li-Chun Wang,et al. Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach , 2019, IEEE Transactions on Network Science and Engineering.
[83] Richard Demo Souza,et al. A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.
[84] Theodore S. Rappaport,et al. Path loss models for 5G millimeter wave propagation channels in urban microcells , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).
[85] Alexandros Kaloxylos,et al. 5G Radio Access Network Architecture: Design Guidelines and Key Considerations , 2016, IEEE Communications Magazine.
[86] Ranjan K. Mallik,et al. A Machine Learning Approach for Power Allocation in HetNets Considering QoS , 2018, 2018 IEEE International Conference on Communications (ICC).
[87] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[88] Pei Liu,et al. Full duplex cellular systems: will doubling interference prevent doubling capacity? , 2015, IEEE Communications Magazine.
[89] Mohsen Guizani,et al. Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.
[90] M. Thangavel,et al. A Review on Digital Sphere Threats and Vulnerabilities , 2016 .
[91] Xinyu Gu,et al. D2D power control based on supervised and unsupervised learning , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).
[92] Alfredo De Santis,et al. Network anomaly detection with the restricted Boltzmann machine , 2013, Neurocomputing.
[93] Ekram Hossain,et al. Deep Learning for Radio Resource Allocation in Multi-Cell Networks , 2018, IEEE Network.
[94] Gerhard Fettweis,et al. Experimental testbed for 5G cognitive radio access in 4G LTE cellular systems , 2014, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM).
[95] Tamma Bheemarjuna Reddy,et al. Software Defined Wireless Networks: A Survey of Issues and Solutions , 2017, Wireless Personal Communications.
[96] Tarik Taleb,et al. AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions , 2020, IEEE Network.
[97] Sungjoo Lee,et al. Technology-Based New Service Idea Generation for Smart Spaces: Application of 5G Mobile Communication Technology , 2016 .
[98] Usha Devi Gandhi,et al. A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases , 2017, Comput. Electr. Eng..
[99] Xiqi Gao,et al. Cellular architecture and key technologies for 5G wireless communication networks , 2014, IEEE Communications Magazine.
[100] SHAHID MUMTAZ,et al. Direct mobile-to-mobile communication: Paradigm for 5G , 2014, IEEE Wireless Communications.
[101] Mohsen Guizani,et al. Reinforcement learning for resource provisioning in the vehicular cloud , 2016, IEEE Wireless Communications.
[102] Vasilis Friderikos,et al. Realizing the Tactile Internet: Haptic Communications over Next Generation 5G Cellular Networks , 2015, IEEE Wireless Communications.
[103] M. Shamim Hossain,et al. Telesurgery Robot Based on 5G Tactile Internet , 2018, Mob. Networks Appl..
[104] Zhifeng Zhao,et al. A Machine Learning Based Intrusion Detection System for Software Defined 5G Network , 2017, ArXiv.
[105] Robert W. Heath,et al. 5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning , 2018, 2018 Information Theory and Applications Workshop (ITA).
[106] Junaid Qadir,et al. Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks , 2017, ArXiv.
[107] Vikas Kumar,et al. Reduced out-of-band radiation-based filter optimization for UFMC systems in 5G , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).
[108] Paul Geladi,et al. Principal Component Analysis , 1987, Comprehensive Chemometrics.
[109] Christos Bouras,et al. SDN & NFV in 5G: Advancements and challenges , 2017, 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN).
[110] Bin Cao,et al. An advanced spectrum allocation algorithm for the across-cell D2D communication in LTE network with higher throughput , 2016 .
[111] Raouf Boutaba,et al. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities , 2018, Journal of Internet Services and Applications.
[112] Hao Peng,et al. Ultra dense network: Challenges, enabling technologies and new trends , 2016 .
[113] Walid Saad,et al. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.
[114] Jukka K. Nurminen,et al. Adaptive Root Cause Analysis for Self-Healing in 5G Networks , 2017, 2017 International Conference on High Performance Computing & Simulation (HPCS).
[115] Vahid Shah-Mansouri,et al. RAT selection based on association probability in 5G heterogeneous networks , 2017, 2017 IEEE Symposium on Communications and Vehicular Technology (SCVT).
[116] Tapani Ristaniemi,et al. Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era , 2018, IEEE Wireless Communications.
[117] Gerhard Fettweis,et al. GFDM - Generalized Frequency Division Multiplexing , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.
[118] Mohsen Guizani,et al. Exploiting Client-Side Collected Measurements to Perform QoS Assessment of IaaS , 2015, IEEE Transactions on Mobile Computing.
[119] Young-June Choi,et al. Survey of Promising Technologies for 5G Networks , 2016, Mob. Inf. Syst..
[120] Jianhua Zhang,et al. The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model , 2016, China Communications.
[121] Haifeng Li,et al. Error aware multiple vertical planes based visual localization for mobile robots in urban environments , 2015, Science China Information Sciences.
[122] Gerhard Fettweis,et al. 5G-Enabled Tactile Internet , 2016, IEEE Journal on Selected Areas in Communications.
[123] Po-Chiang Lin,et al. Data-Driven Handover Optimization in Next Generation Mobile Communication Networks , 2016, Mob. Inf. Syst..
[124] Tristan Henderson,et al. CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth , 2005, IEEE Pervasive Comput..
[125] Shancang Li,et al. 5G Internet of Things: A survey , 2018, J. Ind. Inf. Integr..
[126] Jeffrey G. Andrews,et al. What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.
[127] Suhaidi Hassan,et al. 5G: The Next Wave of Digital Society Challenges and Current Trends , 2017 .
[128] Klaus David,et al. 6G Vision and Requirements: Is There Any Need for Beyond 5G? , 2018, IEEE Vehicular Technology Magazine.
[129] Kentaro Ishizu,et al. Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks , 2017, IEEE Access.
[130] Theodore S. Rappaport,et al. Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges , 2014, Proceedings of the IEEE.
[131] Filip De Turck,et al. A machine learning-based framework for preventing video freezes in HTTP adaptive streaming , 2017, J. Netw. Comput. Appl..
[132] Frank Schaich,et al. 5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications , 2014, IEEE Communications Magazine.
[133] Firooz B. Saghezchi,et al. Towards a secure network architecture for smart grids in 5G era , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).
[134] David Silver,et al. Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.
[135] Xiang Chen,et al. Security in Mobile Edge Caching with Reinforcement Learning , 2018, IEEE Wireless Communications.
[136] Yang Yang,et al. A Supervised Learning Based QoS Assurance Architecture for 5G Networks , 2019, IEEE Access.
[137] Hamed Haddadi,et al. Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[138] Naser Al-Falahy,et al. Technologies for 5G Networks: Challenges and Opportunities , 2017, IT Professional.
[139] Yoshikazu Miyanaga,et al. QoS-Oriented Mode, Spectrum, and Power Allocation for D2D Communication Underlaying LTE-A Network , 2016, IEEE Transactions on Vehicular Technology.
[140] Mohsen Guizani,et al. Millimeter-wave multimedia communications: challenges, methodology, and applications , 2015, IEEE Communications Magazine.
[141] Jing Zhang,et al. 5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds , 2018, IEEE Communications Magazine.
[142] Jeffrey H. Reed,et al. Artificial Intelligence Defined 5G Radio Access Networks , 2018, IEEE Communications Magazine.
[143] W. Art Chaovalitwongse,et al. Machine Learning Algorithms in Bipedal Robot Control , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[144] Xing Zhang,et al. A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.
[145] Muhammad Ali Imran,et al. How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare? , 2017, Future Internet.
[146] K. B. Letaief,et al. A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.
[147] Geng Wu,et al. 5G Network Capacity: Key Elements and Technologies , 2014, IEEE Vehicular Technology Magazine.
[148] Kenneth Stewart,et al. Enabling technologies and architectures for 5G wireless , 2014, 2014 IEEE MTT-S International Microwave Symposium (IMS2014).
[149] Theodore S. Rappaport,et al. Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.
[150] Giancarlo Fortino,et al. Integration of agent-based and Cloud Computing for the smart objects-oriented IoT , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).
[151] Xuemin Shen,et al. Cloud assisted HetNets toward 5G wireless networks , 2015, IEEE Communications Magazine.
[152] Xiaofei Wang,et al. Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges , 2015, IEEE Access.
[153] Jose Ordonez-Lucena,et al. Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges , 2017, IEEE Communications Magazine.
[154] Min Chen,et al. A 5G Cognitive System for Healthcare , 2017, Big Data Cogn. Comput..
[155] Mohammad S. Obaidat,et al. Coalition Games for Spatio-Temporal Big Data in Internet of Vehicles Environment: A Comparative Analysis , 2015, IEEE Internet of Things Journal.
[156] Baha Uddin Kazi,et al. Next generation wireless cellular networks: ultra-dense multi-tier and multi-cell cooperation perspective , 2018, Wireless Networks.
[157] Jing Wang,et al. Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off , 2014, IEEE Communications Magazine.
[158] Timothy L. Brown,et al. Speech-Based Interaction with In-Vehicle Computers: The Effect of Speech-Based E-Mail on Drivers' Attention to the Roadway , 2001, Hum. Factors.
[159] Meixia Tao,et al. Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services , 2014, IEEE Transactions on Communications.
[160] Seizo Onoe. 1.3 Evolution of 5G mobile technology toward 1 2020 and beyond , 2016, 2016 IEEE International Solid-State Circuits Conference (ISSCC).
[161] Mohsen Guizani,et al. Network function virtualization in 5G , 2016, IEEE Communications Magazine.
[162] Marimuthu Palaniswami,et al. Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..
[163] Dan Wang,et al. From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies , 2018, IEEE Communications Magazine.
[164] Michael S. Berger,et al. Cloud RAN for Mobile Networks—A Technology Overview , 2015, IEEE Communications Surveys & Tutorials.
[165] Nei Kato,et al. State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.
[166] Woongsup Lee,et al. Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network , 2018, IEEE Communications Letters.
[167] Chunxiao Jiang,et al. Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks , 2015, IEEE Wireless Communications.
[168] Félix J. García Clemente,et al. On the performance of a deep learning-based anomaly detection system for 5G networks , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[169] Wansu Lim,et al. Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions , 2019, IEEE Access.
[170] Gang Feng,et al. Proactive content caching by exploiting transfer learning for mobile edge computing , 2018, Int. J. Commun. Syst..
[171] Jitender Grover,et al. Optimization in Edge Computing and Small-Cell Networks , 2018, Edge Computing.
[172] Mugen Peng,et al. Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues , 2018, IEEE Communications Surveys & Tutorials.
[173] Takaya Miyazawa,et al. Consideration On Automation of 5G Network Slicing with Machine Learning , 2018, 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K).
[174] Özgü Alay,et al. 5GENESIS: The Genesis of a flexible 5G Facility , 2018, 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).
[175] Haralabos C. Papadopoulos,et al. Massive MIMO Technologies and Challenges towards 5G , 2016, IEICE Trans. Commun..
[176] Yanjiao Chen,et al. From QoS to QoE: A Tutorial on Video Quality Assessment , 2015, IEEE Communications Surveys & Tutorials.
[177] Yasir Mehmood,et al. Mobile M2M communication architectures, upcoming challenges, applications, and future directions , 2015, EURASIP Journal on Wireless Communications and Networking.
[178] Zhu Han,et al. Wireless Device-to-Device Communications and Networks , 2015 .
[179] Oriol Sallent,et al. On Learning and Exploiting Time Domain Traffic Patterns in Cellular Radio Access Networks , 2016, MLDM.
[180] Navrati Saxena,et al. Efficient Cell Outage Detection in 5G HetNets Using Hidden Markov Model , 2016, IEEE Communications Letters.
[181] Moses Garuba,et al. Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network , 2017, IEEE Transactions on Industrial Informatics.
[182] Zhu Han,et al. Machine Learning Paradigms for Next-Generation Wireless Networks , 2017, IEEE Wireless Communications.
[183] Nadeem Javaid,et al. Intelligence in IoT-Based 5G Networks: Opportunities and Challenges , 2018, IEEE Communications Magazine.
[184] Vladimir A. Oleshchuk,et al. Remote Patient Monitoring Within a Future 5G Infrastructure , 2011, Wirel. Pers. Commun..
[185] Shiwen Mao,et al. CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.
[186] Bao-Shuh Paul Lin,et al. Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management , 2018, 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft).
[187] Sheng Chen,et al. Social-aware D2D communications: qualitative insights and quantitative analysis , 2014, IEEE Communications Magazine.
[188] James M. Tien,et al. Internet of Things, Real-Time Decision Making, and Artificial Intelligence , 2017, Annals of Data Science.
[189] Lajos Hanzo,et al. User-Centric C-RAN Architecture for Ultra-Dense 5G Networks: Challenges and Methodologies , 2017, IEEE Communications Magazine.
[190] Jing Xu,et al. Challenges of System-Level Simulations and Performance Evaluation for 5G Wireless Networks , 2014, IEEE Access.
[191] Mohsen Guizani,et al. 5G Millimeter-Wave Antenna Array: Design and Challenges , 2017, IEEE Wireless Communications.
[192] Petri Mähönen,et al. The Importance of Being Earnest: Performance of Modulation Classification for Real RF Signals , 2018, 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).
[193] Ying-Chang Liang,et al. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[194] Sijing Zhang,et al. Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management , 2018, IEEE Transactions on Network and Service Management.
[195] Zhang Zhengquan,et al. Key techniques for 5G wireless communications:network architecture, physical layer, and MAC layer perspectives , 2015 .
[196] Bodhaswar T. Maharaj,et al. PAPR reduction in FBMC systems using a smart gradient-project active constellation extension method , 2014, 2014 21st International Conference on Telecommunications (ICT).
[197] Ping Zhang,et al. An effective approach to 5G: Wireless network virtualization , 2015, IEEE Communications Magazine.
[198] Taoka Hidekazu,et al. Scenarios for 5G mobile and wireless communications: the vision of the METIS project , 2014, IEEE Communications Magazine.
[199] Xuemin Shen,et al. Enabling device-to-device communications in millimeter-wave 5G cellular networks , 2015, IEEE Communications Magazine.
[200] Muhammad Ali Imran,et al. Cell Fault Management Using Machine Learning Techniques , 2019, IEEE Access.
[201] Félix J. García Clemente,et al. A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks , 2018, IEEE Access.
[202] Ekram Hossain,et al. Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective , 2014, IEEE Wireless Communications.
[203] Faqir Zarrar Yousaf,et al. NFV and SDN—Key Technology Enablers for 5G Networks , 2017, IEEE Journal on Selected Areas in Communications.
[204] Fei Long,et al. Autonomic mobile networks: The use of artificial intelligence in wireless communications , 2017, 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM).
[205] Ekram Hossain,et al. 5G cellular: key enabling technologies and research challenges , 2015, IEEE Instrumentation & Measurement Magazine.
[206] Amitava Ghosh,et al. Recent advancements in M2M communications in 4G networks and evolution towards 5G , 2015, 2015 18th International Conference on Intelligence in Next Generation Networks.
[207] Petar Popovski,et al. Ultra-reliable communication in 5G wireless systems , 2014, 1st International Conference on 5G for Ubiquitous Connectivity.
[208] Athanasios V. Vasilakos,et al. IEEE Access Special Section Editorial: Artificial Intelligence Enabled Networking , 2015, IEEE Access.