integration of wimax and wifi optimal pricing for bandwidth sharing dusit niyato and ekram hossain,...

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

2

Outline

• Introduction

• Major Research Issues and The Related Approaches

• Pricing for Bandwidth Sharing in An Integrated WIMAX/WIFI Network

• Conclusions

3

An integrated WiMAX/WiFi network

4

Protocol Adaptation and QoS Support

• 802.16e– unsolicited granted service– polling service– best effort service

• 802.11e– low-priority traffic– high-priority traffic

5

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

6

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

8

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