integration of wimax and wifi optimal pricing for bandwidth sharing dusit niyato and ekram hossain,...
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Integration of WiMAX and WiFi Optimal Pricing for Bandwidth Sharing
Dusit Niyato and Ekram Hossain, TRLabs and University of ManitobaIEEE Communications Magazine • May 2007
報告者:李宗穎
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Outline
• Introduction
• Major Research Issues and The Related Approaches
• Pricing for Bandwidth Sharing in An Integrated WIMAX/WIFI Network
• Conclusions
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An integrated WiMAX/WiFi network
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Protocol Adaptation and QoS Support
• 802.16e– unsolicited granted service– polling service– best effort service
• 802.11e– low-priority traffic– high-priority traffic
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QoS support in an integrated WiMAX/WiFi network
• per-flow approach– guarantee QoS for individual flows– complexity is high
• aggregate approach– reduce this overhead by grouping multiple flows w
ith similar QoS requirements together and servicing them as a single traffic class
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Pricing
• pricing issue relates to the control of radio resource usage from an economic point of view– Optimization-Based Pricing– Game-Theory-Based Pricing
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Optimization-Based Pricing
• Goal : maximize utility– the wired network to maximize system utility
• the rate is a function of price
– price-based distributed algorithm for rate adaptation in wireless networks
• both rate and reliability performances
• Disadvantage– may not satisfy all the related entities individually
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Game-Theory-Based Pricing (1/2)
• game-theoretic formulation aims at providing individually optimal solutions– suitable for systems with multiple entities– service providers want to maximize their profit– users want to achieve their best QoS performance
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Game-Theory-Based Pricing (2/2)
• Three major components– the players– the strategies of the players– the payoffs for the players
• Nash equilibrium– no player can increase his/her payoff by choosing a
different strategy
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System Description
• the WiMAX BSs and WiFi routers are operated by different service providers
• the WiMAX service provider charges the WiFi networks with adjustable pricing
• bandwidth sharing and pricing model uses a genetic algorithm for learning to choose the best strategy
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Revenue and Elastic Demand (1/3)
SSN
i
siiii
s bDeaR1
)()( )],([
• WiMAX BS charges different prices to different WiFi APs/routers depending on the bandwidth demand from WiFi clients
D(λi,bi(s)) : queuing delay
λi : traffic arrival ratebi(s) : allocated bandwidthNSS : total number of SSsai : indicates the fixed revenueei : decreasing rate of revenue due to the queuing delay
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Revenue and Elastic Demand (2/3)
• a linear demand function expressed as follows
• The revenue of the WiFi network k is obtained
bj(Pk(r)) = cj – djPk
(r))
bj(Pk(r)) : the bandwidth demand of node j
Pk(r) : the price charged at WiFi AP/router k
cj : the fixed bandwidth demanddj : elasticity of the demand function
)(
1
)()()( )(r
kN
j
rkj
rk
rk PbPR
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Revenue and Elastic Demand (3/3)
• Finally, the cost is calculated from
)(
1
)()()()( )(r
kN
j
rk
rkj
bsk
rk FPbPC
Pk(bs) : the price charged by the WiMAX BS to the WiFi AP/router k
Nk(r) : the number of WiFi nodes served by router k
Fk(r) : a fixed cost for a WiFi router
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Stackelberg Game and Profit Maximization (1/2)
• The players– The WiMAX BS and WiFi APs/routers
• The strategies– WiMAX BS : the price Pk
(bs) charged to the WiFi APs
– WiFi APs : the required bandwidth
• The payoffs– WiMAX BS and WiFi APs/routers, the payoffs are th
e corresponding profits
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Stackelberg Game and Profit Maximization (2/2)
• Given the price charged by the WiMAX BS Pk(bs), the
profit of AP k is
• WiMAX BS can adjust the price Pk(bs) charged to rout
er k to achieve the highest payoff
πk(r) = Rk
(r) – Ck(r)
rN
k
rk
sbs RR1
)()()(
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Genetic algorithm for Stackelberg game for bandwidth sharing
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Simulation Parameter
BS Type TDMA/TDD
Frame duration 5ms
Bandwidth 20MHz
Modulation QPSK (1/2)
SS number 10
WiFi Router Serve Number 4 + 6
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Profit function of the WiMAX BS
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Price and bandwidth sharing at the equilibrium under different traffic
loads at the subscriber stations.
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Price and bandwidth sharing at the equilibrium under different numbers of Wi
Fi nodes served by WiFi router two
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Conclusions
• Game theory has been used to analyze and obtain the optimal pricing for bandwidth sharing between a WiMAX BS and WiFi APs/routers