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