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It has been an exciting journey since the mobile communications and artificial intelligence (AI) were conceived in 1983 and 1956. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5th generation mobile communication technology (5G) and AI is beginning to significantly transform the core communication infrastructure, network management, and vertical applications. The paper first outlined the individual roadmaps of mobile communications and AI in the early stage, with a concentration to review the era from 3rd generation mobile communication technology (3G) to 5G when AI and mobile communications started to converge. With regard to telecommunications AI, the progress of AI in the ecosystem of mobile communications was further introduced in detail, including network infrastructure, network operation and management, business operation and management, intelligent applications towards business supporting system (BSS) & operation supporting system (OSS) convergence, verticals and private networks, etc. Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations. Towards the next decade, the prospective roadmap of telecommunications AI was forecasted. In line with 3rd generation partnership project (3GPP) and International Telecommunication Union Radiocommunication Sector (ITU-R) timeline of 5G & 6th generation mobile communication technology (6G), the network intelligence following 3GPP and open radio access network (O-RAN) routes, experience and intent-based network management and operation, network AI signaling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS & OSS convergence, evolution from service level agreement (SLA) to experience level agreement (ELA), and intelligent private network for verticals were further explored. The paper is concluded with the vision that AI will reshape the future beyond 5G (B5G)/6G landscape, and we need pivot our research and development (R&D), standardizations, and ecosystem to fully take the unprecedented opportunities.
It has been an exciting journey since the mobile communications and artificial intelligence (AI) were conceived in 1983 and 1956. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5th generation mobile communication technology (5G) and AI is beginning to significantly transform the core communication infrastructure, network management, and vertical applications. The paper first outlined the individual roadmaps of mobile communications and AI in the early stage, with a concentration to review the era from 3rd generation mobile communication technology (3G) to 5G when AI and mobile communications started to converge. With regard to telecommunications AI, the progress of AI in the ecosystem of mobile communications was further introduced in detail, including network infrastructure, network operation and management, business operation and management, intelligent applications towards business supporting system (BSS) & operation supporting system (OSS) convergence, verticals and private networks, etc. Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations. Towards the next decade, the prospective roadmap of telecommunications AI was forecasted. In line with 3rd generation partnership project (3GPP) and International Telecommunication Union Radiocommunication Sector (ITU-R) timeline of 5G & 6th generation mobile communication technology (6G), the network intelligence following 3GPP and open radio access network (O-RAN) routes, experience and intent-based network management and operation, network AI signaling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS & OSS convergence, evolution from service level agreement (SLA) to experience level agreement (ELA), and intelligent private network for verticals were further explored. The paper is concluded with the vision that AI will reshape the future beyond 5G (B5G)/6G landscape, and we need pivot our research and development (R&D), standardizations, and ecosystem to fully take the unprecedented opportunities.
R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H. Zhang, Intelligent 5G: When cellular networks meet artificial intelligence, IEEE Wirel. Commun., vol. 24, no. 5, pp. 175–183, 2017.
G. E. Hinton, S. Osindero, and Y. W. Teh, A fast learning algorithm for deep belief nets, Neural Comput., vol. 18, no. 7, pp. 1527–1554, 2006.
T. F. Bresnahan and M. Trajtenberg, General purpose technologies “Engines of growth”? J. Econom., vol. 65, no. 1, pp. 83–108, 1995.
R. Shafin, L. Liu, V. Chandrasekhar, H. Chen, J. Reed, and J. C. Zhang, Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G, IEEE Wirel. Commun., vol. 27, no. 2, pp. 212–217, 2020.
J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955, AI Mag., vol. 27, no. 4, pp. 12–14, 2006.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol. 323, no. 6088, pp. 533–536, 1986.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, Nature, vol. 518, no. 7540, pp. 529–533, 2015.
Y. Okumura, Field strength and its variability in VHF and UHF land-mobile radio service, Rev. Electr. Commun. Lab., vol. 16, pp. 825–873, 1968.
M. Hata, Empirical formula for propagation loss in land mobile radio services, IEEE Trans. Veh. Technol., vol. 29, no. 3, pp. 317–325, 1980.
D. J. Young and N. C. Beaulieu, The generation of correlated Rayleigh random variates by inverse discrete Fourier transform, IEEE Trans. Commun., vol. 48, no. 7, pp. 1114–1127, 2000.
A. Aragón-Zavala, B. Belloul, V. Nikolopoulos, and S. R. Saunders, Accuracy evaluation analysis for indoor measurement-based radio-wave-propagation predictions, IEE Proc. -Microwaves, Antennas Propag., vol. 153, no. 1, pp. 67–74, 2006.
J. M. Johnson and V. Rahmat-Samii, Genetic algorithms in engineering electromagnetics, IEEE Antennas Propag. Mag., vol. 39, no. 4, pp. 7–21, 1997.
O. Boyabatli and I. Sabuncuoglu, Parameter selection in genetic algorithms, J. Syst., Cybern. Inf., vol. 4, no. 2, p. 78, 2004.
S. S. Mosleh, L. Liu, C. Sahin, Y. Zheng, and Y. Yi, Brain-inspired wireless communications: Where reservoir computing meets MIMO-OFDM, IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 10, pp. 4694–4708, 2018.
F. Liang, C. Shen, and F. Wu, An iterative BP-CNN architecture for channel decoding, IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, pp. 144–159, 2018.
Y. Ouyang, Z. Li, L. Su, W. Lu, and Z. Lin, Application behaviors driven self-organizing network (SON) for 4G LTE networks, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 1, pp. 3–14, 2020.
M. Qin, Q. Yang, N. Cheng, J. Li, W. Wu, R. R. Rao, and X. Shen, Learning-aided multiple time-scale SON function coordination in ultra-dense small-cell networks, IEEE Trans. Wirel. Commun., vol. 18, no. 4, pp. 2080–2092, 2019.
A. Engels, M. Reyer, X. Xu, R. Mathar, J. Zhang, and H. Zhuang, Autonomous self-optimization of coverage and capacity in LTE cellular networks, IEEE Trans. Veh. Technol., vol. 62, no. 5, pp. 1989–2004, 2013.
S. Berger, A. Fehske, P. Zanier, I. Viering, and G. Fettweis, Online antenna tilt-based capacity and coverage optimization, IEEE Wirel. Commun. Lett., vol. 3, no. 4, pp. 437–440, 2014.
R. Razavi, S. Klein, and H. Claussen, A fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks, Bell Labs Tech. J., vol. 15, no. 3, pp. 153–175, 2010.
B. Partov, D. J. Leith, and R. Razavi, Utility fair optimization of antenna tilt angles in LTE networks, IEEE/ACM Trans. Netw., vol. 23, no. 1, pp. 175–185, 2015.
D. Lee, S. Zhou, X. Zhong, Z. Niu, X. Zhou, and H. Zhang, Spatial modeling of the traffic density in cellular networks, IEEE Wirel. Commun., vol. 21, no. 1, pp. 80–88, 2014.
A. J. Fehske, H. Klessig, J. Voigt, and G. P. Fettweis, Concurrent load-aware adjustment of user association and antenna tilts in self-organizing radio networks, IEEE Trans. Veh. Technol., vol. 62, no. 5, pp. 1974–1988, 2013.
S. Berger, M. Simsek, A. Fehske, P. Zanier, I. Viering, and G. Fettweis, Joint downlink and uplink tilt-based self-organization of coverage and capacity under sparse system knowledge, IEEE Trans. Veh. Technol., vol. 65, no. 4, pp. 2259–2273, 2016.
A. Awada, B. Wegmann, I. Viering, and A. Klein, A SON-based algorithm for the optimization of inter-RAT handover parameters, IEEE Trans. Veh. Technol., vol. 62, no. 5, pp. 1906–1923, 2013.
O. C. Iacoboaiea, B. Sayrac, S. B. Jemaa, and P. Bianchi, SON coordination in heterogeneous networks: A reinforcement learning framework, IEEE Trans. Wirel. Commun., vol. 15, no. 9, pp. 5835–5847, 2016.
J. Sánchez-González, J. Pérez-Romero, and O. Sallent, A rule-based solution search methodology for self-optimization in cellular networks, IEEE Commun. Lett., vol. 18, no. 12, pp. 2189–2192, 2014.
P. Bhaumik, S. Zhang, P. Chowdhury, S. S. Lee, J. H. Lee, and B. Mukherjee, Software-defined optical networks (SDONs): A survey, Photon. Netw. Commun., vol. 28, no. 1, pp. 4–18, 2014.
A. Caballero, R. Borkowski, I. de Miguel, R. J. Durán, J. C. Aguado, N. Fernández, T. Jiménez, I. Rodríguez, D. Sánchez, R. M. Lorenzo, et al., heterogeneous and reconfigurable optical networks: The CHRON project, J. Lightwave Technol., vol. 32, no. 13, pp. 2308–2323, 2014.
J. Mata, I. de Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, Artificial intelligence (AI) methods in optical networks: A comprehensive survey, Opt. Switch. Netw., vol. 28, pp. 43–57, 2018.
L. Gupta, T. Salman, M. Zolanvari, A. Erbad, and R. Jain, Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study, Comput. Netw., vol. 1065, p. 106950, 2019.
S. Magids, A. Zorfas, and D. Leemon, The new science of customer emotions, Harv. Bus. Rev., vol. 76, no. 11, pp. 66–74, 2015.
F. F. Reichheld, The one number you need to grow, Harv. Bus. Rev., vol. 81, no. 12, pp. 46–54, 2003.
C. Stahlkopf, Where net promoter score goes wrong, Harv. Bus. Rev., vol. 10, 2019.
D. M. Gutierrez-Estevez, M. Gramaglia, A. De Domenico, G. Dandachi, S. Khatibi, D. Tsolkas, I. Balan, A. Garcia-Saavedra, U. Elzur, and Y. Wang, Artificial intelligence for elastic management and orchestration of 5G networks, IEEE Wirel. Commun., vol. 26, no. 5, pp. 134–141, 2019.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, Federated machine learning: Concept and applications, ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, p. 12, 2019.
S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, 2010.
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