On the potential of ensemble regression techniques for future mobile network planning
07543784.pdf (3,285Mb) (Restricted access) Request copy
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
Planning of current and future mobile networks is becoming increasingly complex due to the heterogeneity of deployments, which feature not only macrocells, but also an underlying layer of small cells whose deployment is not fully under the control of the operator. In this paper, we focus on selecting the most appropriate Quality of Service (QoS) prediction techniques for assisting network operators in planning future dense deployments. We propose to use machine learning as a tool to extract the relevant information from the huge amount of data generated in current 4G and future 5G networks during normal operation, which is then used to appropriately plan networks. In particular, we focus on radio measurements to develop correlative statistical models with the purpose of improving QoS-based network planning. In this direction, we combine multiple learners by building ensemble methods and use them to do regression in a reduced space rather than in the original one. We then compare the QoS prediction accuracy of various approaches that take as input the 3GPP Minimization of Drive Tests (MDT) measurements collected throughout a heterogeneous network and analyse their trade-offs. We also explain how the collected data is processed and used to predict QoS expressed in terms of Physical Resource Block (PRB)/ Megabit (MB) transmitted. This metric was selected because of the interest it may have for operators in planning, since it relates lower layer resources with their impact in terms of QoS up in the protocol stack, hence closer to the end-user.
CitationMoysen, J., Giupponi, L., J. M. On the potential of ensemble regression techniques for future mobile network planning. A: IEEE Symposium on Computers and Communications. "2016 IEEE Symposium on Computers and Communication (ISCC) took place 27 June-1 July 2016 in Messina, Italy". Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1-7.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder