Machine Learning Based Handover Management for Improved QoE in LTE
| Main Authors: | Ali, Zoraze, Mangues-Bafalluy, Josep, Giupponi, Lorenza |
|---|---|
| Format: | Proceeding Journal |
| Terbitan: |
, 2016
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/439005 |
Daftar Isi:
- This paper presents a machine learning based handover management scheme for LTE to improve the Quality of Experience (QoE) of the user in the presence of obstacles. We show that, in this scenario, a state-of-the-art handover algorithm is unable to select the appropriate target cell for handover, since it always selects the target cell with the strongest signal without taking into account the perceived QoE of the user after the handover. In contrast, our scheme learns from past experience how the QoE of the user is affected when the handover was done to a certain eNB. Our performance evaluation shows that the proposed scheme substantially improves the number of completed downloads and the average download time compared to state-of-the-art. Furthermore, its performance is close to an optimal approach in the coverage region affected by an obstacle.
- Grant numbers : The work of Z. Ali is also supported by grant5GNORM (TEC2014-60491-R).© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.