TY - GEN
T1 - AI Enabled Resource Allocation in Future Mobile Networks
AU - Mughal, Umer Rehman
AU - Ahmed Khan, Manzoor
AU - Beg, Azam
AU - Mughal, Ghulam Qadir
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - The recent past has advocated immense flexibility in the control and elasticity in the resources of the mobile networks. The emerging application domains including autonomous driving, eHealth, smart grid, etc. position the need for the right communication stretch at a pivotal level. It goes without saying that the network operators will experience the dynamic demands like never before owing to an extremely dynamic device layer i.e., IoT. An obvious consequence of this is the uncertainty in the demand estimation and capacity planning of the communication infrastructure. This paper studies the concept of dynamic demand estimation using AI approaches. We start with learning over the mobility and activity patterns of a single user and evaluate the performance of different machine learning (ML) approaches, for example, classification, regression, and clustering. We then move on to more realistic settings, where the learning is carried out for population and autonomous driving. To do so, we use the data-set from Ernst-Reuter-Platz collected as part a Berlin City project that made the data openly available.
AB - The recent past has advocated immense flexibility in the control and elasticity in the resources of the mobile networks. The emerging application domains including autonomous driving, eHealth, smart grid, etc. position the need for the right communication stretch at a pivotal level. It goes without saying that the network operators will experience the dynamic demands like never before owing to an extremely dynamic device layer i.e., IoT. An obvious consequence of this is the uncertainty in the demand estimation and capacity planning of the communication infrastructure. This paper studies the concept of dynamic demand estimation using AI approaches. We start with learning over the mobility and activity patterns of a single user and evaluate the performance of different machine learning (ML) approaches, for example, classification, regression, and clustering. We then move on to more realistic settings, where the learning is carried out for population and autonomous driving. To do so, we use the data-set from Ernst-Reuter-Platz collected as part a Berlin City project that made the data openly available.
KW - Artificial Intelligence
KW - Autonomous Driving
KW - Mobility and Activity Management and Machine Learning.
UR - http://www.scopus.com/inward/record.url?scp=85086756843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086756843&partnerID=8YFLogxK
U2 - 10.1109/NOMS47738.2020.9110397
DO - 10.1109/NOMS47738.2020.9110397
M3 - Conference contribution
AN - SCOPUS:85086756843
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Y2 - 20 April 2020 through 24 April 2020
ER -