TY - GEN
T1 - A Machine Learning Approach to User Assignment in 5G Networks
AU - Kouris, Michael
AU - Kokkinos, Vasileios
AU - Gkamas, Apostolos
AU - Pouyioutas, Philippos
AU - Bouras, Christos
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the advancement of wireless networks the data needs of the wireless internet have become so great, and the use of 4G and 5G so ubiquitous, that challenges arise in the availability and distribution of resources. In this paper we examine an application of Machine Learning and subsequently Neural Networks to offer a solution to this problem. The, now more than ever, profound availability of processing power greatly empowers and makes the deployment of these tools easier than ever, even in problems where in the past their application would not be feasible. The use of machine learning techniques to distribute resources in wireless networks is investigated and contrasted to a traditional algorithmic method. Scenarios are also entertained in which such approaches might be applicable.
AB - With the advancement of wireless networks the data needs of the wireless internet have become so great, and the use of 4G and 5G so ubiquitous, that challenges arise in the availability and distribution of resources. In this paper we examine an application of Machine Learning and subsequently Neural Networks to offer a solution to this problem. The, now more than ever, profound availability of processing power greatly empowers and makes the deployment of these tools easier than ever, even in problems where in the past their application would not be feasible. The use of machine learning techniques to distribute resources in wireless networks is investigated and contrasted to a traditional algorithmic method. Scenarios are also entertained in which such approaches might be applicable.
KW - 5G
KW - Deep Learning
KW - MIMO
KW - Neural Networks
KW - User Assignment
UR - http://www.scopus.com/inward/record.url?scp=85142411842&partnerID=8YFLogxK
U2 - 10.1109/GIIS56506.2022.9936981
DO - 10.1109/GIIS56506.2022.9936981
M3 - Conference contribution
AN - SCOPUS:85142411842
T3 - 2022 Global Information Infrastructure and Networking Symposium, GIIS 2022
SP - 35
EP - 39
BT - 2022 Global Information Infrastructure and Networking Symposium, GIIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Global Information Infrastructure and Networking Symposium, GIIS 2022
Y2 - 26 September 2022 through 28 September 2022
ER -