Introduction System Model A two-layered optimization approach to improve the user perceived QoS in heterogeneous networks. Top layer views the problem of prediction of the network dependent on user preferences. User preferences are offered bit rate, price, mobility support and reputation. At bottom layer, network operator hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR in order to optimize the network coverage to support users who are not serviced adequately. System Performance Evaluation Analytic Hierarchy Process (AHP) Reputation Select the suitable network Price Bit rate Mobility WiMAX WLAN Figure 1 Network Selection Hierarchy of Dependencies Simple Additive Weighting (SAW) Problem of “Selecting the most suitable network” and the user preferred criteria are represented into hierarchy of dependencies in Figure 1. Analytic Hierarchy Process (AHP) is used to calculate the weight for each criteria based on user priorities (on 1 to 9 scale). The comparison matrix is created by pair-wise comparisons between the considered criteria Mobile-Android Desktop- Web Figure 2 Android Application to collect data for considered criteria from the network The developed Android mobile application installed in HTC handsets will be used to collect the network related data (as shown in Figure 2) and SAW is used to calculate the weight of each network with respect to the considered criteria. Finally the heterogeneous user received payoff for selecting each network is calculated and the network with highest payoff is selected. This network selection gives an initial partition showing the proportion of users who prefer WiMAX and Wi-Fi. 2 The strategy available to each user is to select WLAN or WiMAX Category A Category D Area covered by both networks Population Figure 3 Simulation Scenario J Applications Dynamic Cell Coverage Control Cognitive Radio & Wireless Networks Cooperating Networks Publications Haris Pervaiz, Haibo Mei, Peng Jiang and John Bigham. (2010) Enhanced cooperation in heterogeneous wireless networks using coverage adjustment. In Proceedings of the 6th International Wireless Communications and Mobile Computing Conference in session of Cognitive radio communications and networks (cooperative and cognitive networks) in Caen, France. Pages 241-245. Haris Pervaiz and John Bigham. (2009) Game Theoretical Formulation of Network Selection in Competing Wireless Networks: An Analytic Hierarchy Process Model. In Proceedings of Third International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST09) in Cardiff, Wales. Pages 292-297. First International Summer School on Cognitive Wireless Communications 12 th -16 th July 2011
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Introduction
System Model
A two-layered optimization approach to improve the user
perceived QoS in heterogeneous networks.
Top layer views the problem of prediction of the network
dependent on user preferences.
User preferences are offered bit rate, price, mobility
support and reputation.
At bottom layer, network operator hypothetically,
reconfigures the network, subject to the network
constraints of bandwidth and acceptable SNR in order to
optimize the network coverage to support users who are
not serviced adequately.
System Performance
Evaluation
Analytic Hierarchy
Process (AHP)
Reputation
Select the suitable
network
Price Bit rate Mobility
WiMAX WLAN
Figure 1 Network Selection Hierarchy of Dependencies
Simple Additive
Weighting (SAW)
Problem of “Selecting the most suitable network” and
the user preferred criteria are represented into hierarchy
of dependencies in Figure 1.
Analytic Hierarchy Process (AHP) is used to calculate theweight for each criteria based on user priorities (on 1 to 9scale).
The comparison matrix is created by pair-wise
comparisons between the considered criteria
Mobile-Android Desktop- Web
Figure 2 Android Application to collect data for considered criteria from the
network
The developed Android mobile application installed in HTC handsets will be used
to collect the network related data (as shown in Figure 2) and SAW is used to
calculate the weight of each network with respect to the considered criteria.
Finally the heterogeneous user received payoff for selecting each network is
calculated and the network with highest payoff is selected. This network selection
gives an initial partition showing the proportion of users who prefer WiMAX and
Wi-Fi.2
The strategy available to each user is to select WLAN or WiMAX
Category A
Category D
Area covered by both
networksPopulation
Figure 3 Simulation Scenario
J
Applications
Dynamic Cell Coverage Control
Cognitive Radio & Wireless Networks
Cooperating NetworksPublications
Haris Pervaiz, Haibo Mei, Peng Jiang and John Bigham. (2010)Enhanced cooperation in heterogeneous wireless networksusing coverage adjustment. In Proceedings of the 6thInternational Wireless Communications and Mobile ComputingConference in session of Cognitive radio communications andnetworks (cooperative and cognitive networks) in Caen, France.Pages 241-245.
Haris Pervaiz and John Bigham. (2009) Game TheoreticalFormulation of Network Selection in Competing WirelessNetworks: An Analytic Hierarchy Process Model. In Proceedingsof Third International Conference on Next Generation MobileApplications, Services and Technologies (NGMAST09) in Cardiff,Wales. Pages 292-297.
First International Summer School on Cognitive Wireless Communications 12th-16th July 2011
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