A Service-Centric Q-Learning Algorithm for Mobility Robustness Optimization in LTE

Due to the diversity of mobile services and rising user expectations, mobile network management has changed its focus from Quality of Service (QoS) to Quality of Experience (QoE). As a consequence, classical network optimization procedures must be updated accordingly. One of these optimization proce...

Full description

Saved in:
Bibliographic Details
Published in:IEEE eTransactions on network and service management Vol. 18; no. 3; pp. 3541 - 3555
Main Authors: Mari-Altozano, Maria Luisa, Mwanje, Stephen S., Ramirez, Salvador Luna, Toril, Matias, Sanneck, Henning, Gijon, Carolina
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Handover
Heuristic algorithms
Long Term Evolution
Machine learning
Machine learning algorithms
Network management systems
neural network
Optimization
Q-learning
Quality of experience
Quality of service architectures
Robustness
self organizing networks
Tuning
ISSN:1932-4537, 1932-4537
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Due to the diversity of mobile services and rising user expectations, mobile network management has changed its focus from Quality of Service (QoS) to Quality of Experience (QoE). As a consequence, classical network optimization procedures must be updated accordingly. One of these optimization procedures is Mobility Robustness Optimization (MRO), whose aim is to improve HandOver (HO) performance by reducing HO failures. In this work, a novel QoE-aware MRO algorithm is proposed considering a multi-service scenario. Unlike previous approaches, whose aim is to increase successful handover rates, the optimization aim in this work is two-folded: to improve cell edge QoE while improving successful handover rates in the whole network. For this purpose, the handover trigger point, defined by the pair of HO control parameters HO margin and Time to Trigger, are tuned on a per-adjacency basis according to QoE and HO failure measurements. Method assessment is based on a dynamic system-level simulator implementing a realistic LTE scenario with multiple services. Results show that the proposed QoE-aware MRO algorithm improves cell edge QoE throughout the network while increasing the percentage of successful handovers compared to traditional approaches.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2021.3073244