competition in cognitive radio networks: spectrum leasing and innovation
Post on 07-Apr-2018
219 Views
Preview:
TRANSCRIPT
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
1/16
Competition in cognitive radionetworks: spectrum leasing andinnovation
CCNC 2011
Las Vegas, 11 January
Luis Guijarro
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
2/16
2
Agenda
Objective Model
Method
Results and analysis
Further work
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
3/16
3
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
4/16
4
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
5/16
5
Model Competition
Income
n users
Flat-rate
Costs
Operating costs
Profits
ssss
pppp
Cbpnp
Cbpnp
=
+=
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
6/16
6
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
7/16
7
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.
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
8/16
8
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
9/16
9
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
=
=
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
10/16
10
Results and analysis Constant parameters
# of users, n
Total spectrum W
Leasing pricep
Variable parameters
Leased spectrum b
Spectral efficiency k
(s)
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
11/16
11
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
12/16
12
Results and analysis Impact of leased
spectrum
Objective
Optimum amount of
leased spectrum
Experiment
b/Wvaries from 10%
to 90%
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
13/16
13
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
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
14/16
14
Results and analysis Impact of technology
innovation
Objective
Impact of increasing
k(s)
Experiment
k(s)/k(p) varies from 1
to 5
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
15/16
15
Results and analysis Impact of technology
innovation
Results
User utility
increases
PoA tends to the
unity
Conclusion Users are better off
when entrant
innovates
-
8/6/2019 Competition in cognitive radio networks: spectrum leasing and innovation
16/16
16
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
top related