Skip to main content
Springer Nature Link
Log in

A survey of 5G network systems: challenges and machine learning approaches

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless networks then we give a deep insight into a set of 5G challenges and research opportunities for machine learning (ML) techniques to manage these challenges. The first part of the paper is devoted to overview the fifth-generation of cellular networks, explaining its requirements as well as its key technologies, their challenges and its forthcoming architecture. The second part is devoted to present a basic overview of ML techniques that are nowadays applied to cellular networks. The last part discusses the most important related works which propose ML solutions in order to overcome 5G challenges.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. A backhaul network is an intermediary that enables the data transmission and reception between core networks, or macro base station and small base stations. It can be a wired or wireless link.

References

  1. Monserrat JF, Mange G, Braun V, Tullberg H, Zimmermann G, Bulakci Ö (2015) Metis research advances towards the 5G mobile and wireless system definition. EURASIP J Wirel Commun Netw 2015(1):53

    Google Scholar 

  2. Al-Falahy N, Alani OY (2017) Technologies for 5G networks: challenges and opportunities. IT Prof 19(1):12–20

    Google Scholar 

  3. Onoe S (2016) 1.3 evolution of 5G mobile technology toward 1 2020 and beyond. In: 2016 IEEE international solid-state circuits conference (ISSCC). IEEE, pp 23–28

  4. Valente KP, Imran MA, Onireti O, Souza RD (2017) A survey of machine learning techniques applied to self organizing cellular networks. IEEE Commun Surv Tutor 19:2392–2431

    Google Scholar 

  5. Intelligence G (2014) Understanding 5G: perspectives on future technological advancements in mobile. White paper, pp 1–26

  6. Agiwal M, Roy A, Saxena N (2016) Next generation 5G wireless networks: a comprehensive survey. IEEE Commun Surv Tutor 18(3):1617–1655

    Google Scholar 

  7. Talwar S, Choudhury D, Dimou K, Aryafar E, Bangerter B, Stewart K (2014) Enabling technologies and architectures for 5G wireless. In: 2014 IEEE MTT-S international microwave symposium (IMS2014). IEEE, pp 1–4

  8. Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC (2014) What will 5G be? IEEE J Sel Areas Commun 32(6):1065–1082

    Google Scholar 

  9. Osseiran A, Boccardi F, Braun V, Kusume K, Marsch P, Maternia M, Queseth O, Schellmann M, Schotten H, Taoka H et al (2014) Scenarios for 5G mobile and wireless communications: the vision of the metis project. IEEE Commun Mag 52(5):26–35

    Google Scholar 

  10. Li QC, Niu H, Papathanassiou AT, Wu G (2014) 5G network capacity: key elements and technologies. IEEE Veh Technol Mag 9(1):71–78

    Google Scholar 

  11. Hossain E, Hasan M (2015) 5G cellular: key enabling technologies and research challenges. IEEE Instrum Meas Mag 18(3):11–21

    Google Scholar 

  12. Papadopoulos H, Wang C, Bursalioglu O, Hou X, Kishiyama Y (2016) Massive mimo technologies and challenges towards 5G. IEICE Trans Commun 99(3):602–621

    Google Scholar 

  13. Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) Massive mimo for next generation wireless systems. IEEE Commun Mag 52(2):186–195

    Google Scholar 

  14. Zhang Y, Yu R, Nekovee M, Liu Y, Xie S, Gjessing S (2012) Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw 26(3):6–13

    Google Scholar 

  15. Goudar SI, Hassan S, Habbal A (2017) 5G: The next wave of digital society challenges and current trends, Journal of Telecommunication. Electron Comput Eng (JTEC) 9(1–2):63–66

    Google Scholar 

  16. Alnoman A, Anpalagan A (2017) Towards the fulfillment of 5G network requirements: technologies and challenges. Telecommun Syst 65(1):101–116

    Google Scholar 

  17. Wu G, Yang C, Li S, Li GY (2015) Recent advances in energy-efficient networks and their application in 5G systems. IEEE Wirel Commun 22(2):145–151

    Google Scholar 

  18. Mell P, Grance T et al (2011) The nist definition of cloud computing

  19. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18

    Google Scholar 

  20. Nguyen V-G, Brunstrom A, Grinnemo K-J, Taheri J (2017) Sdn/nfv-based mobile packet core network architectures: a survey. IEEE Commun Surv Tutor 19(3):1567–1602

    Google Scholar 

  21. Abdelwahab S, Hamdaoui B, Guizani M, Znati T (2016) Network function virtualization in 5G. IEEE Commun Mag 54(4):84–91

    Google Scholar 

  22. Rangan S, Rappaport TS, Erkip E (2014) Millimeter wave cellular wireless networks: potentials and challenges. arXiv preprint arXiv:1401.2560

  23. Ma Z, Zhang Z, Ding Z, Fan P, Li H (2015) Key techniques for 5G wireless communications: network architecture, physical layer, and mac layer perspectives. Sci China Inf Sci 58(4):1–20

    Google Scholar 

  24. Zheng G (2015) Joint beamforming optimization and power control for full-duplex mimo two-way relay channel. IEEE Trans Signal Process 63(3):555–566

    MathSciNet  MATH  Google Scholar 

  25. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358

    Google Scholar 

  26. Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5:6757–6779

    Google Scholar 

  27. Letaief KB, Chen W, Shi Y, Zhang J, Zhang Y-JA (2019) The roadmap to 6g-AI empowered wireless networks. arXiv preprint arXiv:1904.11686

  28. Arfaoui G, Vilchez JMS, Wary J-P (2017) Security and resilience in 5G: current challenges and future directions. In: 2017 IEEE Trustcom/BigDataSE/ICESS. IEEE, pp 1010–1015

  29. Klautau A, Batista P, Prelcic N, Wang Y, Heath R (2016) 5G mimo data for machine learning: application to beam-selection using deep learning. In: 2018 proceedings of information theory and applications workshop (ITA), pp 1–9

  30. Kafle VP, Fukushima Y, Martinez-Julia P, Miyazawa T (2018) Consideration on automation of 5G network slicing with machine learning. In: 2018 ITU Kaleidoscope: machine learning for a 5G future (ITU K). IEEE, pp 1–8

  31. International Telecommunication Union (ITU) (2017) Focus Group on Machine Learning for Future Networks including 5G (FG-ML5G). https://www.itu.int/en/ITU-T/focusgroups/ml5g/Pages/default.aspx. Accessed Nov 2017

  32. 5G Public Private Partnership (5G-PPP) (2015) CogNet-building an intelligent system of insights and action for 5G network management. http://www.cognet.5g-ppp.eu/. Accessed 1 July 2015

  33. Wang X, Li X, Leung VC (2015) Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access 3:1379–1391

    Google Scholar 

  34. Kibria MG, Nguyen K, Villardi GP, Zhao O, Ishizu K, Kojima F (2018) Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access 6:32328–32338

    Google Scholar 

  35. Long F, Li N, Wang Y (2017) Autonomic mobile networks: the use of artificial intelligence in wireless communications. In: 2017 2nd international conference on advanced robotics and mechatronics (ICARM). IEEE, pp 582–586

  36. Moysen J, Giupponi L (2018) From 4G to 5G: self-organized network management meets machine learning. Comput Commun 129:248–268

    Google Scholar 

  37. Pérez-Romero J, Sánchez-González J, Sallent O, Agustí R (2016) On learning and exploiting time domain traffic patterns in cellular radio access networks. In: International conference on machine learning and data mining in pattern recognition. Springer, pp 501–515

  38. Alsharif MH, Nordin R (2017) Evolution towards fifth generation (5G) wireless networks: current trends and challenges in the deployment of millimetre wave, massive mimo, and small cells. Telecommun Syst 64(4):617–637

    Google Scholar 

  39. Li S, Da Xu L, Zhao S (2018) 5G internet of things: a survey. J Ind Inf Integr 10:1–9

    Google Scholar 

  40. Kazi BU, Wainer GA (2019) Next generation wireless cellular networks: ultra-dense multi-tier and multi-cell cooperation perspective. Wirel Netw 25(4):2041–2064

    Google Scholar 

  41. Gupta A, Jha RK (2015) A survey of 5G network: architecture and emerging technologies. IEEE Access 3:1206–1232

    Google Scholar 

  42. Marsch P, Da Silva I, Bulakci O, Tesanovic M, El Ayoubi SE, Rosowski T, Kaloxylos A, Boldi M (2016) 5G radio access network architecture: design guidelines and key considerations. IEEE Commun Mag 54(11):24–32

    Google Scholar 

  43. Elijah O, Leow CY, Rahman TA, Nunoo S, Iliya SZ (2016) A comprehensive survey of pilot contamination in massive mimo-5G system. IEEE Commun Surv Tutor 18(2):905–923

    Google Scholar 

  44. Ahmed I, Khammari H, Shahid A, Musa A, Kim KS, De Poorter E, Moerman I (2018) A survey on hybrid beamforming techniques in 5G: architecture and system model perspectives. IEEE Commun Surv Tutor 20(3060):3097

    Google Scholar 

  45. Chin WH, Fan Z, Haines R (2014) Emerging technologies and research challenges for 5G wireless networks. IEEE Wirel Commun 21(2):106–112

    Google Scholar 

  46. Zhang C, Patras P, Haddadi H (2019) Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor 21:2224–2287

    Google Scholar 

  47. Zhu G, Zan J, Yang Y, Qi X (2019) A supervised learning based qos assurance architecture for 5G networks. IEEE Access 7:43598–43606

    Google Scholar 

  48. Jiang C, Zhang H, Ren Y, Han Z, Chen K-C, Hanzo L (2017) Machine learning paradigms for next-generation wireless networks. IEEE Wirel Commun 24(2):98–105

    Google Scholar 

  49. Javaid N, Sher A, Nasir H, Guizani N (2018) Intelligence in iot-based 5G networks: opportunities and challenges. IEEE Commun Mag 56(10):94–100

    Google Scholar 

  50. Li R, Zhao Z, Zhou X, Ding G, Chen Y, Wang Z, Zhang H (2017) Intelligent 5G: when cellular networks meet artificial intelligence. IEEE Wirel Commun 24(5):175–183

    Google Scholar 

  51. Zhang J (2016) The interdisciplinary research of big data and wireless channel: a cluster-nuclei based channel model. China Commun 13(Supplement 2):14–26

    Google Scholar 

  52. Bogale TE, Wang X, Le LB (2018) Machine intelligence techniques for next-generation context-aware wireless networks. arXiv preprint arXiv:1801.04223

  53. Latif S, Qadir J, Farooq S, Imran MA (2017) How 5G wireless (and concomitant technologies) will revolutionize healthcare? Future Internet 9(4):93

    Google Scholar 

  54. Qadir J, Yau K-LA, Imran MA, Ni Q, Vasilakos AV (2015) Ieee access special section editorial: Artificial intelligence enabled networking. IEEE Access 3:3079–3082

    Google Scholar 

  55. Chen M, Challita U, Saad W, Yin C, Debbah M (2017) Machine learning for wireless networks with artificial intelligence: a tutorial on neural networks. arXiv preprint arXiv:1710.02913

  56. Salahuddin MA, Al-Fuqaha A, Guizani M (2016) Reinforcement learning for resource provisioning in the vehicular cloud. IEEE Wirel Commun 23(4):128–135

    Google Scholar 

  57. Latif S, Pervez F, Usama M, Qadir J (2017) Artificial intelligence as an enabler for cognitive self-organizing future networks. arXiv preprint arXiv:1702.02823

  58. Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178

    Google Scholar 

  59. Fadlullah Z, Tang F, Mao B, Kato N, Akashi O, Inoue T, Mizutani K (2017) State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun Surv Tutor 19(4):2432–2455

    Google Scholar 

  60. Bui N, Cesana M, Hosseini SA, Liao Q, Malanchini I, Widmer J (2017) A survey of anticipatory mobile networking: context-based classification, prediction methodologies, and optimization techniques. IEEE Commun Surv Tutor 19(3):1790–1821

    Google Scholar 

  61. Le NT, Hossain MA, Islam A, Kim D-Y, Choi Y-J, Jang YM (2016) Survey of promising technologies for 5G networks. Mob Inf Syst 2016:2676589

    Google Scholar 

  62. Boutaba R, Salahuddin MA, Limam N, Ayoubi S, Shahriar N, Estrada-Solano F, Caicedo OM (2018) A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Internet Serv Appl 9(1):16

    Google Scholar 

  63. Sultan K, Ali H, Zhang Z (2018) Big data perspective and challenges in next generation networks. Future Internet 10(7):56

    Google Scholar 

  64. Xie J, Song Z, Li Y, Zhang Y, Yu H, Zhan J, Ma Z, Qiao Y, Zhang J, Guo J (2018) A survey on machine learning-based mobile big data analysis: challenges and applications. Wirel Commun Mob Comput 2018:8738613

    Google Scholar 

  65. Xie J, Song Z, Li Y, Ma Z (2018) Mobile big data analysis with machine learning. arXiv preprint arXiv:1808.00803

  66. Buda TS, Assem H, Xu L, Raz D, Margolin U, Rosensweig E, Lopez DR, Corici M-I, Smirnov M, Mullins R et al (2016) Can machine learning aid in delivering new use cases and scenarios in 5G? In: NOMS 2016–2016 IEEE/IFIP network operations and management symposium. IEEE, pp 1279–1284

  67. Wang Y, Xu J, Jiang L (2014) Challenges of system-level simulations and performance evaluation for 5G wireless networks. IEEE Access 2:1553–1561

    Google Scholar 

  68. Yao M, Sohul M, Marojevic V, Reed JH (2019) Artificial intelligence defined 5G radio access networks. IEEE Commun Mag 57(3):14–20

    Google Scholar 

  69. Boccardi F, Heath RW, Lozano A, Marzetta TL, Popovski P (2014) Five disruptive technology directions for 5G. IEEE Commun Mag 52(2):74–80

    Google Scholar 

  70. Razavizadeh SM, Ahn M, Lee I (2014) Three-dimensional beamforming: a new enabling technology for 5G wireless networks. IEEE Signal Process Mag 31(6):94–101

    Google Scholar 

  71. Farhang-Boroujeny B, Moradi H (2016) Ofdm inspired waveforms for 5G. IEEE Commun Surv Tutor 18(4):2474–2492

    Google Scholar 

  72. Mitra RN, Agrawal DP (2015) 5G mobile technology: a survey. ICT Express 1(3):132–137

    Google Scholar 

  73. Wunder G, Jung P, Kasparick M, Wild T, Schaich F, Chen Y, Ten Brink S, Gaspar I, Michailow N, Festag A et al (2014) 5Gnow: non-orthogonal, asynchronous waveforms for future mobile applications. IEEE Commun Mag 52(2):97–105

    Google Scholar 

  74. Schaich F, Wild T (2014) Waveform contenders for 5G–OFDM vs. FBMC vs. UFMC. In: 2014 6th international symposium on communications, control and signal processing (ISCCSP). IEEE, pp 457–460

  75. van der Neut N, Maharaj B, de Lange FH, Gonzalez G, Gregorio F, Cousseau J (2014) PAPR reduction in FBMC systems using a smart gradient-project active constellation extension method. In: 2014 21st international conference on telecommunications (ICT). IEEE, pp 134–139

  76. Danneberg M, Datta R, Festag A, Fettweis G (2014) Experimental testbed for 5G cognitive radio access in 4G LTE cellular systems. In: 2014 IEEE 8th sensor array and multichannel signal processing workshop (SAM). IEEE, pp 321–324

  77. Fettweis GP, Krondorf M, Bittner S (2009) GFDM-generalized frequency division multiplexing. In: VTC. Spring, pp 1–4

  78. Mukherjee M, Shu L, Kumar V, Kumar P, Matam R (2015) Reduced out-of-band radiation-based filter optimization for UFMC systems in 5G. In: 2015 international wireless communications and mobile computing conference (IWCMC). IEEE, pp 1150–1155

  79. Song L, Niyato D, Han Z, Hossain E (2015) Wireless device-to-device communications and networks. Cambridge University Press, Cambridge

    Google Scholar 

  80. Mumtaz S, Huq KMS, Rodriguez J (2014) Direct mobile-to-mobile communication: paradigm for 5G. IEEE Wirel Commun 21(5):14–23

    Google Scholar 

  81. Fortuna C, Mohorcic M (2009) Trends in the development of communication networks: cognitive networks. Comput Netw 53(9):1354–1376

    Google Scholar 

  82. Aliu OG, Imran A, Imran MA, Evans B (2013) A survey of self organisation in future cellular networks. IEEE Commun Surv Tutor 15(1):336–361

    Google Scholar 

  83. Zhang N, Cheng N, Gamage AT, Zhang K, Mark JW, Shen X (2015) Cloud assisted hetnets toward 5G wireless networks. IEEE Commun Mag 53(6):59–65

    Google Scholar 

  84. Liu Y, She X, Chen P, Zhu J, Yang F (2015) Easy network: the way to go for 5G. China Commun 12(Supplement):113–120

    Google Scholar 

  85. Feng Z, Qiu C, Feng Z, Wei Z, Li W, Zhang P (2015) An effective approach to 5G: wireless network virtualization. IEEE Commun Mag 53(12):53–59

    Google Scholar 

  86. Chowdhury NMK, Boutaba R (2009) Network virtualization: state of the art and research challenges. IEEE Commun Mag 47(7):20–26

    Google Scholar 

  87. Han B, Gopalakrishnan V, Ji L, Lee S (2015) Network function virtualization: challenges and opportunities for innovations. IEEE Commun Mag 53(2):90–97

    Google Scholar 

  88. Hawilo H, Shami A, Mirahmadi M, Asal R (2014) Nfv: state of the art, challenges and implementation in next generation mobile networks (vepc). arXiv preprint arXiv:1409.4149

  89. Open Networking Foundation (ONF) (2014) OpenFlow-enabled SDN and Network Functions Virtualization. https://www.opennetworking.org/wp-content/uploads/2013/05/sb-sdn-nvf-solution.pdf. Accessed 17 Feb 2014

  90. Yousaf FZ, Bredel M, Schaller S, Schneider F (2017) Nfv and sdn-key technology enablers for 5G networks. IEEE J Sel Areas Commun 35(11):2468–2478

    Google Scholar 

  91. Rangisetti AK, Tamma BR (2017) Software defined wireless networks: a survey of issues and solutions. Wirel Pers Commun 97(4):6019–6053

    Google Scholar 

  92. Goyal S, Liu P, Panwar SS, Difazio RA, Yang R, Bala E et al (2015) Full duplex cellular systems: will doubling interference prevent doubling capacity? IEEE Commun Mag 53(5):121–127

    Google Scholar 

  93. Hong S, Brand J, Choi JI, Jain M, Mehlman J, Katti S, Levis P (2014) Applications of self-interference cancellation in 5G and beyond. IEEE Commun Mag 52(2):114–121

    Google Scholar 

  94. Hu YC, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile edge computing—a key technology towards 5G. ETSI White Pap 11(11):1–16

    Google Scholar 

  95. Xiao L, Wan X, Dai C, Du X, Chen X, Guizani M (2018) Security in mobile edge caching with reinforcement learning. arXiv preprint arXiv:1801.05915

  96. Ordonez-Lucena J, Ameigeiras P, Lopez D, Ramos-Munoz JJ, Lorca J, Folgueira J (2017) Network slicing for 5G with sdn/nfv: concepts, architectures, and challenges. IEEE Commun Mag 55(5):80–87

    Google Scholar 

  97. Soleymani B, Zamani A, Rastegar SH, Shah-Mansouri V (2017) RAT selection based on association probability in 5G heterogeneous networks. In: IEEE symposium on communications and vehicular technology (SCVT), pp 1–6

  98. Pérez JS, Jayaweera SK, Lane S (2017) Machine learning aided cognitive RAT selection for 5G heterogeneous networks. In: IEEE international black sea conference on communications and networking (BlackSeaCom), Istanbul, Turkey. IEEE, pp 1–5

  99. Nadeem Q-U-A, Kammoun A, Alouini M-S (2018) Elevation beamforming with full dimension mimo architectures in 5G systems: a tutorial. arXiv preprint arXiv:1805.00225

  100. Wei L, Hu RQ, Qian Y, Wu G (2014) Enable device-to-device communications underlaying cellular networks: challenges and research aspects. IEEE Commun Mag 52(6):90–96

    Google Scholar 

  101. Li Y, Wu T, Hui P, Jin D, Chen S (2014) Social-aware d2d communications: Qualitative insights and quantitative analysis. IEEE Commun Mag 52(6):150–158

    Google Scholar 

  102. Maimó LF, Clemente FJG, Pérez MG, Pérez GM (2017) On the performance of a deep learning-based anomaly detection system for 5G networks. In: 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). IEEE, pp 1–8

  103. Bouras C, Kollia A, Papazois A (2017) SDN & NFV in 5G: advancements and challenges. In: 2017 20th conference on innovations in clouds, internet and networks (ICIN). IEEE, pp 107–111

  104. Sun S, Gong L, Rong B, Lu K (2015) An intelligent sdn framework for 5G heterogeneous networks. IEEE Commun Mag 53(11):142–147

    Google Scholar 

  105. Chih-Lin I, Han S, Xu Z, Sun Q, Pan Z (2016) 5G: rethink mobile communications for 2020+. Philos Trans R Soc A Math Phys Eng Sci 374(2062):20140432

    Google Scholar 

  106. MacCartney GR, Zhang J, Nie S, Rappaport TS (2013) Path loss models for 5G millimeter wave propagation channels in urban microcells. In: 2013 IEEE global communications conference (GLOBECOM), pp 3948–3953

  107. Shafi M, Molisch AF, Smith PJ, Haustein T, Zhu P, De Silva P, Tufvesson F, Benjebbour A, Wunder G (2017) 5G: a tutorial overview of standards, trials, challenges, deployment, and practice. IEEE J Sel Areas Commun 35(6):1201–1221

    Google Scholar 

  108. You X, Zhang C, Tan X, Jin S, Wu H (2019) Ai for 5G: research directions and paradigms. Sci China Inf Sci 62(2):21301

    Google Scholar 

  109. Shariatmadari H, Ratasuk R, Iraji S, Laya A, Taleb T, Jäntti R, Ghosh A (2015) Machine-type communications: current status and future perspectives toward 5G systems. IEEE Commun Mag 53(9):10–17

    Google Scholar 

  110. Tullberg H, Popovski P, Li Z, Uusitalo MA, Hoglund A, Bulakci O, Fallgren M, Monserrat JF (2016) The metis 5G system concept: meeting the 5G requirements. IEEE Commun Mag 54(12):132–139

    Google Scholar 

  111. Queseth O, Bulakci Ö, Spapis P, Bisson P, Marsch P, Arnold P, Rost P, Wang Q, Blom R, Salsano S, et al (2017) 5G ppp architecture working group: view on 5G architecture (version 2.0, December 2017)

  112. Nawaz SJ, Sharma SK, Wyne S, Patwary MN, Asaduzzaman M (2019) Quantum machine learning for 6g communication networks: state-of-the-art and vision for the future. IEEE Access 7:46317–46350

    Google Scholar 

  113. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Google Scholar 

  114. Alliance N (2015) 5G white paper, Next generation mobile networks, white paper, pp 1–125

  115. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454

    Google Scholar 

  116. Fortino G, Guerrieri A, Russo W, Savaglio C (2014) Integration of agent-based and cloud computing for the smart objects-oriented IoT. In: Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design (CSCWD). IEEE, pp 493–498

  117. Wang D, Chen D, Song B, Guizani N, Yu X, Du X (2018) From iot to 5G i-iot: the next generation iot-based intelligent algorithms and 5G technologies. IEEE Commun Mag 56(10):114–120

    Google Scholar 

  118. Ratasuk R, Prasad A, Li Z, Ghosh A, Uusitalo MA (2015) Recent advancements in M2M communications in 4G networks and evolution towards 5G. In: 2015 18th international conference on intelligence in next generation networks. IEEE, pp 52–57

  119. Kumar N, Misra S, Rodrigues JJ, Obaidat MS (2015) Coalition games for spatio-temporal big data in internet of vehicles environment: a comparative analysis. IEEE Internet Things J 2(4):310–320

    Google Scholar 

  120. Lee JD, Caven B, Haake S, Brown TL (2001) Speech-based interaction with in-vehicle computers: the effect of speech-based e-mail on drivers’ attention to the roadway. Hum Factors 43(4):631–640

    Google Scholar 

  121. Oleshchuk V, Fensli R (2011) Remote patient monitoring within a future 5G infrastructure. Wirel Pers Commun 57(3):431–439

    Google Scholar 

  122. West DM (2016) How 5G technology enables the health internet of things. Brook Cent Technol Innov 3:1–20

    Google Scholar 

  123. Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inform 7(4):529–539

    Google Scholar 

  124. Jeong S, Jeong Y, Lee K, Lee S, Yoon B (2016) Technology-based new service idea generation for smart spaces: application of 5G mobile communication technology. Sustainability 8(11):1211

    Google Scholar 

  125. Rappaport TS, Sun S, Mayzus R, Zhao H, Azar Y, Wang K, Wong GN, Schulz JK, Samimi M, Gutierrez F (2013) Millimeter wave mobile communications for 5G cellular: it will work!. IEEE access 1:335–349

    Google Scholar 

  126. Simsek M, Aijaz A, Dohler M, Sachs J, Fettweis G (2016) 5G-enabled tactile internet. IEEE J Sel Areas Commun 34(3):460–473

    Google Scholar 

  127. Aijaz A, Dohler M, Aghvami AH, Friderikos V, Frodigh M (2016) Realizing the tactile internet: haptic communications over next generation 5G cellular networks. IEEE Wirel Commun 24(2):82–89

    Google Scholar 

  128. Popovski P (2014) Ultra-reliable communication in 5G wireless systems. In: 1st international conference on 5G for ubiquitous connectivity. IEEE, pp 146–151

  129. Hossain E, Rasti M, Tabassum H, Abdelnasser A (2014) Evolution towards 5G multi-tier cellular wireless networks: an interference management perspective. arXiv preprint arXiv:1401.5530

  130. Wang C-X, Haider F, Gao X, You X-H, Yang Y, Yuan D, Aggoune HM, Haas H, Fletcher S, Hepsaydir E (2014) Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun Mag 52(2):122–130

    Google Scholar 

  131. Zhang H, Jiang C, Beaulieu NC, Chu X, Wen X, Tao M (2014) Resource allocation in spectrum-sharing of dma femtocells with heterogeneous services. IEEE Trans Commun 62(7):2366–2377

    Google Scholar 

  132. Hao P, Yan X, Yu-Ngok R, Yuan Y (2016) Ultra dense network: challenges enabling technologies and new trends. China Commun 13(2):30–40

    Google Scholar 

  133. Ge X, Tu S, Mao G, Wang C-X, Han T (2015) 5G ultra-dense cellular networks. arXiv preprint arXiv:1512.03143

  134. Grover J, Garimella RM (2019) Optimization in edge computing and small-cell networks. In: Edge computing. Springer, pp 17–31

  135. Hong X, Wang J, Wang C-X, Shi J (2014) Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off. IEEE Commun Mag 52(7):46–53

    Google Scholar 

  136. ETSIV (2011) Machine-to-machine communications (m2m): functional architecture. Int Telecommun 102:690 (Union, Geneva, Switzerland, Tech. Rep. TS)

    Google Scholar 

  137. Mehmood Y, Haider N, Imran M, Timm-Giel A, Guizani M (2017) M2m communications in 5G: state-of-the-art architecture, recent advances, and research challenges. IEEE Commun Mag 55(9):194–201

    Google Scholar 

  138. Mehmood Y, Görg C, Muehleisen M, Timm-Giel A (2015) Mobile m2m communication architectures, upcoming challenges, applications, and future directions. EURASIP J Wirel Commun Netw 2015(1):250

    Google Scholar 

  139. Roh W, Seol J-Y, Park J, Lee B, Lee J, Kim Y, Cho J, Cheun K, Aryanfar F (2014) Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results. IEEE Commun Mag 52(2):106–113

    Google Scholar 

  140. Zhang J, Ge X, Li Q, Guizani M, Zhang Y (2017) 5G millimeter-wave antenna array: design and challenges. IEEE Wirel Commun 24(2):106–112

    Google Scholar 

  141. Checko A, Christiansen HL, Yan Y, Scolari L, Kardaras G, Berger MS, Dittmann L (2014) Cloud ran for mobile networks—a technology overview. IEEE Commun Surv Tutor 17(1):405–426

    Google Scholar 

  142. Checko A, Christiansen HL, Yan Y, Scolari L, Kardaras G, Berger MS, Dittmann L (2015) Cloud ran for mobile networks—a technology overview. IEEE Commun Surv Tutor 17(1):405–426

    Google Scholar 

  143. Zhang H, Jiang C, Cheng J, Leung VC (2015) Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks. IEEE Wirel Commun 22(3):92–99

    Google Scholar 

  144. Rost P, Bernardos CJ, De Domenico A, Di Girolamo M, Lalam M, Maeder A, Sabella D, Wübben D (2014) Cloud technologies for flexible 5G radio access networks. IEEE Commun Mag 52(5):68–76

    Google Scholar 

  145. Pan C, Elkashlan M, Wang J, Yuan J, Hanzo L (2018) User-centric c-ran architecture for ultra-dense 5G networks: challenges and methodologies. IEEE Commun Mag 56(6):14–20

    Google Scholar 

  146. ETSI-European Telecommunications Standards Institute (2019) 5G; system architecture for the 5G System (5GS)(3GPP TS 23.501 version 15.5.0 Release 15). https://www.etsi.org/deliver/etsi_ts/123500_123599/123501/15.05.00_60/ts_123501v150500p.pdf. Accessed Apr 2019

  147. The 5G Infraestructure Public Private Partnership (2019) 5G Americas White Paper The Status of Open Source for 5G. http://www.5gamericas.org/files 6915/5070/2509/5G_Americas_White_Paper_The_Status_of_Open_Source_for_5G_Feb_2018.pdf. Accessed Feb 2019

  148. ETSI-European Telecommunications Standards Institute (2018) 5G; system architecture for the 5G system (3GPP TS 23.501 version 15.2.0 Release 15). https://www.etsi.org/deliver/etsi_ts/123500_123599/123501/15.02.00_60/ts_123501v150200p.pdf. Accessed June 2018

  149. Wu D, Wang J, Cai Y, Guizani M (2015) Millimeter-wave multimedia communications: challenges, methodology, and applications. IEEE Commun Mag 53(1):232–238

    Google Scholar 

  150. Comsa I-S, De-Domenico A, Ktenas D (2017) Qos-driven scheduling in 5G radio access networks-a reinforcement learning approach. In: GLOBECOM 2017-2017 IEEE global communications conference. IEEE, pp 1–7

  151. Comşa I-S, Zhang S, Aydin M, Kuonen P, Lu Y, Trestian R, Ghinea G (2018) Towards 5G: a reinforcement learning-based scheduling solution for data traffic management. IEEE Trans Netw Serv Manag 15:1661–1675

    Google Scholar 

  152. Kamel A, Al-Fuqaha A, Guizani M (2014) Exploiting client-side collected measurements to perform QoS assessment of IaaS. IEEE Trans Mobile Comput 14(9):1876–1887

    Google Scholar 

  153. International Telecommunication Union (2017) Vocabulary for performance, quality of service and quality of experience . https://www.itu.int/rec/T-REC-P.10-201711-I. Accessed 13 Nov 2017

  154. European Cooperation in Science and Technology, QoE definition, http://www.cost.eu

  155. International Telecommunication Union, Reference guide to quality of experience assessment methodologies. https://www.itu.int/rec/T-REC-G.1011-201607-I/en

  156. Chen Y, Wu K, Zhang Q (2014) From qos to qoe: a tutorial on video quality assessment. IEEE Commun Surv Tutori 17(2):1126–1165

    Google Scholar 

  157. Qiao J, Shen XS, Mark JW, Shen Q, He Y, Lei L (2015) Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Commun Mag 53(1):209–215

    Google Scholar 

  158. Pierucci L (2015) The quality of experience perspective toward 5G technology. IEEE Wirel Commun 22(4):10–16

    Google Scholar 

  159. Petrangeli S, Wu T, Wauters T, Huysegems R, Bostoen T, De Turck F (2017) A machine learning-based framework for preventing video freezes in http adaptive streaming. J Netw Comput Appl 94:78–92

    Google Scholar 

  160. Imran A, Zoha A, Abu-Dayya A (2014) Challenges in 5G: how to empower son with big data for enabling 5G. IEEE Netw 28(6):27–33

    Google Scholar 

  161. Wu J, Zhang Y, Zukerman M, Yung EK-N (2015) Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey. IEEE Commun Surv Tutor 17(2):803–826

    Google Scholar 

  162. Jaber M, Imran MA, Tafazolli R, Tukmanov A (2017) Energy-efficient SON-based user-centric backhaul scheme. In: 2017 IEEE wireless communications and networking conference workshops (WCNCW). IEEE, pp 1–6

  163. Jaber M, Imran MA, Tafazolli R, Tukmanov A (2016) A distributed son-based user-centric backhaul provisioning scheme. IEEE Access 4:2314–2330

    Google Scholar 

  164. Jaber M, Imran MA, Tafazolli R, Tukmanov A (2016) A multiple attribute user-centric backhaul provisioning scheme using distributed SON. In: 2016 IEEE global communications conference (GLOBECOM). IEEE, pp 1–6

  165. Morocho-Cayamcela ME, Lee H, Lim W (2019) Machine learning for 5G/b5G mobile and wireless communications: potential, limitations, and future directions. IEEE Access 7:137184–137206

    Google Scholar 

  166. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Google Scholar 

  167. Comon P (1994) Independent component analysis, a new concept? Signal Process 36(3):287–314

    MATH  Google Scholar 

  168. Yuan Y, Wan J, Wang Q (2016) Congested scene classification via efficient unsupervised feature learning and density estimation. Pattern Recognit 56:159–169

    Google Scholar 

  169. Amiri R, Mehrpouyan H, Fridman L, Mallik RK, Nallanathan A, Matolak D (2018) A machine learning approach for power allocation in hetnets considering qos. arXiv preprint arXiv:1803.06760

  170. Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: AAAI, vol 2. Phoenix, AZ

  171. Wang S, Chaovalitwongse W, Babuska R (2012) Machine learning algorithms in bipedal robot control. IEEE Tran Syst Man Cybern Part C (Appl Revs) 42(5):728–743

    Google Scholar 

  172. Baştuğ E, Bennis M, Debbah M (2015) A transfer learning approach for cache-enabled wireless networks. In: 2015 13th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt). IEEE, pp 161–166

  173. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Google Scholar 

  174. Muthuramalingam S, Thangavel M, Sridhar S (2016) A review on digital sphere threats and vulnerabilities. In: Combating security breaches and criminal activity in the digital sphere. IGI Global, pp 1–21

  175. Mohr W (2015) The 5G infrastructure public-private partnership. In: Presentation in ITU GSC-19 meeting

  176. Li J, Zhao Z, Li R (2017) Machine learning-based ids for software-defined 5G network. IET Netw 7(2):53–60

    Google Scholar 

  177. Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122:13–23

    Google Scholar 

  178. Maimó LF, Gómez ÁLP, Clemente FJG, Pérez MG, Pérez GM (2018) A self-adaptive deep learning-based system for anomaly detection in 5G networks. IEEE Access 6:7700–7712

    Google Scholar 

  179. Garcia S, Grill M, Stiborek J, Zunino A (2014) An empirical comparison of botnet detection methods. Comput Secur 45:100–123

    Google Scholar 

  180. Zago M, Sánchez VMR, Pérez MG, Pérez GM (2016) Tackling cyber threats with automatic decisions and reactions based on machine-learning techniques. In: Proceedings of the 2nd conference on network management, quality of service and security for 5G networks, Oulu, Finland, pp 1–4

  181. Chang Z, Lei L, Zhou Z, Mao S, Ristaniemi T (2018) Learn to cache: machine learning for network edge caching in the big data era. IEEE Wirel Commun 25(3):28–35

    Google Scholar 

  182. Baldo N, Giupponi L, Mangues-Bafalluy J (2014) Big data empowered self organized networks. In: European wireless 2014; 20th European wireless conference. VDE, pp 1–8

  183. Srinivasa S, Bhatnagar V (2012) Big data analytics: first international conference, BDA 2012, New Delhi, India, December 24-26, 2012, Proceedings, vol 7678. Springer Science & Business Media

  184. Parwez MS, Rawat DB, Garuba M (2017) Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Trans Ind Inform 13(4):2058–2065

    Google Scholar 

  185. Aref MA, Jayaweera SK, Machuzak S (2017) Multi-agent reinforcement learning based cognitive anti-jamming. In: 2017 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1–6

  186. Mulvey D, Foh CH, Imran MA, Tafazolli R (2019) Cell fault management using machine learning techniques. IEEE Access 7:124514–124539

    Google Scholar 

  187. Kumar Y, Farooq H, Imran A (2017) Fault prediction and reliability analysis in a real cellular network. In: 2017 13th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 1090–1095

  188. Mfula H, Nurminen JK (2017) Adaptive root cause analysis for self-healing in 5G networks. In: 2017 international conference on high performance computing & simulation (HPCS). IEEE, pp 136–143

  189. Mismar FB, Evans BL (2018) Deep Q-learning for self-organizing networks fault management and radio performance improvement. In: 2018 52nd asilomar conference on signals, systems, and computers. IEEE, pp 1457–1461

  190. Alias M, Saxena N, Roy A (2016) Efficient cell outage detection in 5G hetnets using hidden markov model. IEEE Commun Lett 20(3):562–565

    Google Scholar 

  191. Yu P, Zhou F, Zhang T, Li W, Feng L, Qiu X (2018) Self-organized cell outage detection architecture and approach for 5G H-CRAN. Wirel Commun Mob Comput 2018:6201386

    Google Scholar 

  192. Farooq H, Parwez MS, Imran A (2015) Continuous time Markov chain based reliability analysis for future cellular networks. In: 2015 IEEE global communications conference (GLOBECOM). IEEE, pp 1–6

  193. Asheralieva A, Miyanaga Y (2016) Qos-oriented mode, spectrum, and power allocation for d2d communication underlaying lte-a network. IEEE Trans Veh Techno 65(12):9787–9800

    Google Scholar 

  194. Zhang L, Xiao M, Wu G, Alam M, Liang Y-C, Li S (2017) A survey of advanced techniques for spectrum sharing in 5G networks. IEEE Wirel Commun 24(5):44–51

    Google Scholar 

  195. Fan Z, Gu X, Nie S, Chen M (2017) D2D power control based on supervised and unsupervised learning. In: 2017 3rd IEEE international conference on computer and communications (ICCC). IEEE, pp 558–563

  196. Rohwer JA, Abdallah CT, El-Osery A (2002) Power control algorithms in wireless communications. In: Digital wireless communications IV, vol 4740. International Society for Optics and Photonics, pp 151–159

  197. Xu J, Gu X, Fan Z (2018) D2D power control based on hierarchical extreme learning machine. In: 2018 IEEE 29th annual international symposium on personal, indoor and mobile radio communications (PIMRC). IEEE, pp 1–7

  198. Wang L-C, Cheng SH (2018) Data-driven resource management for ultra-dense small cells: an affinity propagation clustering approach. IEEE Trans Netw Sci Eng 6:267–279

    Google Scholar 

  199. Balevi E, Gitlin RD (2018) A clustering algorithm that maximizes throughput in 5G heterogeneous F-RAN networks. In: 2018 IEEE international conference on communications (ICC). IEEE, pp 1–6

  200. Alqerm I, Shihada B (2018) Sophisticated online learning scheme for green resource allocation in 5G heterogeneous cloud radio access networks. IEEE Trans Mob Comput 17:2423–2437

    Google Scholar 

  201. AlQerm I, Shihada B (2017) Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks. In: IEEE symposium on personal, indoor and mobile radio communications (PIMRC), pp 1–7

  202. Lin P-C, Casanova LFG, Fatty BK (2016) Data-driven handover optimization in next generation mobile communication networks. Mob Inf Syst 2016:2368427

    Google Scholar 

  203. Khunteta S, Chavva AKR (2017) Deep learning based link failure mitigation. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 806–811

  204. Kanwal K (2017) Increased energy efficiency in lte networks through reduced early handover

  205. Hou T, Feng G, Qin S, Jiang W (2018) Proactive content caching by exploiting transfer learning for mobile edge computing. Int J Commun Syst 31(11):e3706

    Google Scholar 

  206. Shen G, Pei L, Zhiwen P, Nan L, Xiaohu Y (2017) Machine learning based small cell cache strategy for ultra dense networks. In: 2017 9th international conference on wireless communications and signal processing (WCSP). IEEE, pp 1–6

  207. Sadeghi A, Sheikholeslami F, Giannakis GB (2018) Optimal and scalable caching for 5G using reinforcement learning of space-time popularities. IEEE J Sel Top Signal Process 12(1):180–190

    Google Scholar 

  208. Zeydan E, Bastug E, Bennis M, Kader MA, Karatepe IA, Er AS, Debbah M (2016) Big data caching for networking: moving from cloud to edge. IEEE Commun Mag 54(9):36–42

    Google Scholar 

  209. LeCun Y (1998) The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/

  210. Tang F, Fadlullah ZM, Mao B, Kato N (2018) An intelligent traffic load prediction-based adaptive channel assignment algorithm in sdn-iot: a deep learning approach. IEEE Internet Things J 5(6):5141–5154

    Google Scholar 

  211. Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for iot big data and streaming analytics: a survey. IEEE Commun Surv Tutor 20(4):2923–2960

    Google Scholar 

  212. Chen M, Yang J, Zhou J, Hao Y, Zhang J, Youn C-H (2018) 5G-smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun Mag 56(4):16–23

    Google Scholar 

  213. Chen M, Yang J, Hao Y, Mao S, Hwang K (2017) A 5G cognitive system for healthcare. Big Data Cogn Comput 1(1):2

    Google Scholar 

  214. Kumar PM, Gandhi UD (2018) A novel three-tier internet of things architecture with machine learning algorithm for early detection of heart diseases. Comput Electr Eng 65:222–235

    Google Scholar 

  215. Saghezchi FB, Mantas G, Ribeiro J, Al-Rawi M, Mumtaz S, Rodriguez J (2017) Towards a secure network architecture for smart grids in 5G era. In: 13th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 121–126

  216. Miao Y, Jiang Y, Peng L, Hossain MS, Muhammad G (2018) Telesurgery robot based on 5G tactile internet. Mob Netw Appl 23(6):1645–1654

    Google Scholar 

  217. Paolini M, Fili S (2019) Ai and machine learning: Why now?

  218. Benzaid C, Taleb T (2020) Ai-driven zero touch network and service management in 5G and beyond: challenges and research directions. IEEE Netw 34(2):186–194

    Google Scholar 

  219. Sun Y, Peng M, Zhou Y, Huang Y, Mao S (2019) Application of machine learning in wireless networks: key techniques and open issues. IEEE Commun Surv Tutor 21(4):3072–3108

    Google Scholar 

  220. Wang X, Gao L, Mao S (2016) Csi phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3(6):1113–1123

    Google Scholar 

  221. Wang J-B, Wang J, Wu Y, Wang J-Y, Zhu H, Lin M, Wang J (2018) A machine learning framework for resource allocation assisted by cloud computing. IEEE Netw 32(2):144–151

    Google Scholar 

  222. Le L-V, Sinh D, Lin B-SP, Tung L-P (2018) Applying big data, machine learning, and SDN/NFV to 5G traffic clustering, forecasting, and management. In: 2018 4th IEEE conference on network softwarization and workshops (NetSoft). IEEE, pp 168–176

  223. de Vrieze C, Simic L, Mahonen P (2018) The importance of being earnest: performance of modulation classification for real RF signals. In: IEEE international symposium on dynamic spectrum access networks (DySPAN). IEEE, pp 1–5

  224. Koumaras H, Tsolkas D, Gardikis G, Gomez PM, Frascolla V, Triantafyllopoulou D, Emmelmann M, Koumaras V, Osma MLG, Munaretto D et al (2018) 5GENESIS: the genesis of a flexible 5G facility. In: IEEE 23rd international workshop on computer aided modeling and design of communication links and networks (CAMAD). IEEE, pp 1–6

  225. Kotz D, Henderson T (2005) Crawdad: a community resource for archiving wireless data at dartmouth. IEEE Pervasive Comput 4(4):12–14

    Google Scholar 

  226. Lee W, Kim M, Cho D-H (2018) Deep power control: transmit power control scheme based on convolutional neural network. IEEE Commun Lett 22(6):1276–1279

    Google Scholar 

  227. Ahmed KI, Tabassum H, Hossain E (2019) Deep learning for radio resource allocation in multi-cell networks. IEEE Net 33(6):188–195

    Google Scholar 

  228. Tariq F, Khandaker M, Wong K-K, Imran M, Bennis M, Debbah M (2019) A speculative study on 6g. arXiv preprint arXiv:1902.06700

  229. Routray SK, Mohanty S (2019) Why 6G? Motivation and expectations of next-generation cellular networks. arXiv preprint arXiv:1903.04837

  230. Strinati EC, Barbarossa S, Gonzalez-Jimenez JL, Kténas D, Cassiau N, Dehos C (2019) 6g: The next frontier. arXiv preprint arXiv:1901.03239

  231. Saad W, Bennis M, Chen M (2019) A vision of 6g wireless systems: applications, trends, technologies, and open research problems. arXiv preprint arXiv:1902.10265

  232. David K, Berndt H (2018) 6g vision and requirements: is there any need for beyond 5G? IEEE Veh Technol Mag 13(3):72–80

    Google Scholar 

  233. Li R (2018) Towards a new internet for the year 2030 and beyond

  234. Zhang H, Ren Y, Chen K-C, Hanzo L et al (2019) Thirty years of machine learning: the road to pareto-optimal next-generation wireless networks. arXiv preprint arXiv:1902.01946

  235. Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang Y-C, Kim DI (2019) Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tutor 21:3133–3174

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Technology and Smart Systems (LT2S), Digital Research Center of SFAX (CRNS), University of Sfax, Sfax, Tunisia

    Hasna Fourati, Rihab Maaloul & Lamia Chaari

Authors
  1. Hasna Fourati
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rihab Maaloul
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lamia Chaari
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hasna Fourati.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fourati, H., Maaloul, R. & Chaari, L. A survey of 5G network systems: challenges and machine learning approaches. Int. J. Mach. Learn. & Cyber. 12, 385–431 (2021). https://doi.org/10.1007/s13042-020-01178-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01178-4

Keywords