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Wireless Networks

, Volume 19, Issue 8, pp 1807–1828 | Cite as

A personalized QoE-aware handover decision based on distributed reinforcement learning

  • Behrouz Shahgholi Ghahfarokhi
  • Naser Movahhedinia
Article

Abstract

Recent developments in heterogeneous mobile networks and growing demands for variety of real-time and multimedia applications have emphasized the necessity of more intelligent handover decisions. Addressing the context knowledge of mobile devices, users, applications, and networks is the subject of context-aware handoff decision as a recent effort to this aim. However, user perception has not been attended adequately in the area of context-aware handover decision making. Mobile users may have different judgments about the Quality of Service (QoS) depending on their environmental conditions, and personal and psychological characteristics. This reality has been exploited in this paper to introduce a personalized user-centric handoff decision method to decide about the time and target of handover based on User Perceived Quality (UPQ) feedbacks. The UPQ degradations are mainly for the sake of (1) exiting the coverage of the serving Point of Attachment (PoA) or (2) QoS degradation of serving access network. Using UPQ metric, the proposed method obviates the necessity of being aware about rapidly varying network QoS parameters and overcomes the complexity and overhead of gathering and managing some other context information. Moreover, considering the underlying network and geographical map, the proposed method is able to inherently exploit the trajectory information of mobile users for handover decision. UPQ degradation is not only due to the user behaviour, but also due to the behaviours of others users. As such, multi-agent reinforcement learning paradigm has been considered for target PoA selection. The employed decision algorithm is based on WoLF-PHC learning method where UPQ is used as a delayed reward for training. The proposed handoff decision has been implemented under IEEE 802.21 framework using NS2 network simulator. The results have shown better performance of the proposed method comparing to conventional methods assuming regular movement of mobile users.

Keywords

User perceived quality Context-aware handover QoE-aware handover Distributed reinforcement learning 

Abbreviations

AHD

Adaptive handover decision

AHP

Analytic hierarchy process

IS

Information server

MADM

Multi attribute decision making

MARL

Multi agent reinforcement learning

MCHO

Mobile controlled handover

MICS

MIH independent command service

MIES

Media independent event service

MIH

Media independent handover

MIIS

MIH independent information service

MIP

Mobile IP

MN

Mobile node

MOS

Mean opinion score

PHC

Policy hill climbing

PoA

Point of attachment

PQE

Perceived quality evaluator

PSNR

Peak signal to noise ratio

QoCE

Quality of customer experience

QoE

Quality of experience

QoS

Quality of service

QoUE

Quality of user experience

RSS

Received signal strength

SAW

Simple additive weighting

SCM

Spatial conceptual map

SG

Stochastic game

SSNR

Segmental signal to noise ratio

TLV

Type-length-value

UPQ

User perceived quality

WEA

Way elementary areas

WoLF

Win or learn fast

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Behrouz Shahgholi Ghahfarokhi
    • 1
  • Naser Movahhedinia
    • 2
  1. 1.Department of Information Technology EngineeringUniversity of IsfahanIsfahanIran
  2. 2.Department of Computer EngineeringUniversity of IsfahanIsfahanIran

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