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Competition in cognitive radionetworks: spectrum leasing andinnovation

CCNC 2011

Las Vegas, 11 January

Luis Guijarro

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Agenda

Objective Model

Method

Results and analysis

Further work

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Objective Spectrum leasing in a

cognitive radio network

To analyze equilibrium

under competition

between

Primary (PO)

/incumbent

and secondary (SO)/entrant operators

PO

SO

TU

pp

ps

p

bW-b

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Model Spectrum leasing

PO leases b kHz to SO

p and b are set outsidethe model

Competition

la Bertrand

Strategies arepp andps

One-shot game

Quality of service

Spectrum W-b and b

Spectral efficiency k(p)

and k(s)

PO

SO

TU

pp

ps

p

bW-b

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Model Competition

Income

n users

Flat-rate

Costs

Operating costs

Profits

ssss

pppp

Cbpnp

Cbpnp

=

+=

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Model Subscription

Utility

n

n

pUpn

bkU

pUpn

bWkU

p

sss

s

ss

pppp

pp

=

=

)1,(log

),(log

)(

)(

PO

SO

TU

pp

ps

p

bW-b

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Method Game theory

Multi-leader-follower game

The 2 operators fix their pricespi in order tomaximize profits

Each user subscribes to the operator whichoffers higher utility Ui

Solved by backward induction

First, solve subscription game

Then, solve competition game anticipatingthe reaction by users.

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Method

Game theory Multi-leader-follower game

Subscription game

Wardrop equilibrium

Assume n

is high enough

Equilibrium is reached when there is no

incentive to change subscription decision

Assume that every user subscribe to service

)1,(),( = sspp pUpU

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Method

Game theory Multi-leader-follower game

Competition game

Nash equilibrium

The operator does not know the strategy

chosen by the competitor, but space of

available strategies are common knowledge

),(maxarg

),(maxarg

**

**

spsp

s

sppp

p

ppp

ppp

s

p

=

=

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Results and analysis Constant parameters

# of users, n

Total spectrum W

Leasing pricep

Variable parameters

Leased spectrum b

Spectral efficiency k

(s)

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Results and analysis Results

Market share Pricespp,ps User utilities Up=Us

Profits

p,

s Price of Anarchy PoA

Social welfare

the sum of the utilities of all agents in the system (np

Up

+ns

Us

+p

+s)

PoA

the quotient between the maximum value of the social welfare and

the

social welfare obtained at the Nash equilibrium

i.e., PoA

>=1

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Results and analysis Impact of leased

spectrum

Objective

Optimum amount of

leased spectrum

Experiment

b/Wvaries from 10%

to 90%

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Results and analysis Impact of leased spectrum

Results

Maximum user utility

b/W0.45

Minimum PoA reached

b/W0.35

Maximum profits

would drive b/W

towards 1

Conclusion

Maximum b should be

fixed by regulatory

authority

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Results and analysis Impact of technology

innovation

Objective

Impact of increasing

k(s)

Experiment

k(s)/k(p) varies from 1

to 5

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Results and analysis Impact of technology

innovation

Results

User utility

increases

PoA tends to the

unity

Conclusion Users are better off

when entrant

innovates

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Further work

To model bargaining over the leasing pricep and the leased spectrum b

To model the user willingness to pay so that

some users may decide to subscribe toneither PO nor SO