When QoE meets learning: A distributed traffic-processing framework for elastic resource provisioning in HetNets

In heterogeneous networks (HetNets), dynamics of user requirements across the temporal domain lead to the order of magnitude traffic to be processed by macro cells. To achieve high quality of experience (QoE) for users and to perform resource allocation for cells intelligently, we first propose a di...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 167; p. 106904
Main Authors: Yu, Li, Li, Zongpeng, Zhong, Yucun, Ji, Zhenzhou, Liu, Jiangchuan
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 11.02.2020
Elsevier Sequoia S.A
Subjects:
Algorithms
Computer simulation
Embedded systems
Evolutionary algorithms
Frame analysis
Infrastructure
Learning
Machine learning
Profiles
Provisioning
QoE
Reinforcement
Reinforcement learning
Resource allocation
Resource provisioning
Simulation
Software defined infrastructure
Traffic delay
Traffic models
Traffic-processing framework
User requirements
User satisfaction
Traffic-processing framework
Resource provisioning
Software defined infrastructure
QoE
Reinforcement learning
ISSN:1389-1286, 1872-7069
Online Access:Get full text
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Summary:In heterogeneous networks (HetNets), dynamics of user requirements across the temporal domain lead to the order of magnitude traffic to be processed by macro cells. To achieve high quality of experience (QoE) for users and to perform resource allocation for cells intelligently, we first propose a distributed traffic-processing framework (SDVTS) for elastic resource partitioning, to accommodate dynamics from the user-centric and resource-oriented perspectives respectively. Assisted by a software defined infrastructure, SDVTS fulfills the responsibilities of the request-based and push-based services in an interactive loop. Second, we formulate a traffic-processing time model that computes the delay of handling traffic. The non-convex model is decomposed and a dual evolution algorithm is explored to approximate the optimal solution. Furthermore, we introduce a low-complexity reinforcement learning algorithm with the personalized QoE profiling. A distributed algorithm in coalition between user and cell is designed for seamless connection of an advanced reinforcement learning system (ARLS) components and engines embedded in SDVTS. Extensive simulation results with thorough analysis demonstrate that our framework SDVTS dominates in terms of QoE and cell′s system performance when compared with competing approaches.
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ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2019.106904