TY - GEN
T1 - Artificial Intelligence in 5G Technology
T2 - 9th International Conference on Information and Communication Technology Convergence, ICTC 2018
AU - Morocho Cayamcela, Manuel Eugenio
AU - Lim, Wansu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/16
Y1 - 2018/11/16
N2 - A fully operative and efficient 5G network cannot be complete without the inclusion of artificial intelligence (AI) routines. Existing 4G networks with all-IP (Internet Protocol) broadband connectivity are based on a reactive conception, leading to a poorly efficiency of the spectrum. AI and its subcategories like machine learning and deep learning have been evolving as a discipline, to the point that nowadays this mechanism allows fifth-generation (5G) wireless networks to be predictive and proactive, which is essential in making the 5G vision conceivable. This paper is motivated by the vision of intelligent base stations making decisions by themselves, mobile devices creating dynamically-adaptable clusters based on learned data rather than pre-established and fixed rules, that will take us to a improve in the efficiency, latency, and reliability of the current and real-time network applications in general. An exploration of the potential of AI-based solution approaches in the context of 5G mobile and wireless communications technology is presented, evaluating the different challenges and open issues for future research.
AB - A fully operative and efficient 5G network cannot be complete without the inclusion of artificial intelligence (AI) routines. Existing 4G networks with all-IP (Internet Protocol) broadband connectivity are based on a reactive conception, leading to a poorly efficiency of the spectrum. AI and its subcategories like machine learning and deep learning have been evolving as a discipline, to the point that nowadays this mechanism allows fifth-generation (5G) wireless networks to be predictive and proactive, which is essential in making the 5G vision conceivable. This paper is motivated by the vision of intelligent base stations making decisions by themselves, mobile devices creating dynamically-adaptable clusters based on learned data rather than pre-established and fixed rules, that will take us to a improve in the efficiency, latency, and reliability of the current and real-time network applications in general. An exploration of the potential of AI-based solution approaches in the context of 5G mobile and wireless communications technology is presented, evaluating the different challenges and open issues for future research.
KW - 5G Networks
KW - Artificial Intelligence
KW - Deep Learning
KW - IT Convergence
KW - Machine Learning
KW - Next Generation Network
UR - https://www.scopus.com/pages/publications/85059447427
U2 - 10.1109/ICTC.2018.8539642
DO - 10.1109/ICTC.2018.8539642
M3 - Conference contribution
AN - SCOPUS:85059447427
T3 - 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018
SP - 860
EP - 865
BT - 9th International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2018 through 19 October 2018
ER -