Transcript mm.aueb.gr

Incentives-Based Power Control in
Wireless Networks of Autonomous Entities
with Various Degrees of Cooperation
Vaggelis G. Douros
[email protected]
Ph.D. Thesis Defense
Athens, 22.12.2014
1
Examination Committee







2
Prof. George C. Polyzos, AUEB (Advisor)
Advising
Assist. Prof. Stavros Toumpis, AUEB
Committee
Assist. Prof. Vasilios A. Siris, AUEB
Prof. George D. Stamoulis, AUEB
Prof. Lazaros Merakos, NKUA
Assoc. Prof. Stathes Hadjiefthymiades, NKUA
Assist. Prof. Iordanis Koutsopoulos, AUEB
Motivation & Fundamental Ideas
3
Towards the 5G Era (1)
4
4G
5G
Year
2010
2020-2030
Standards
LTE,
LTE-Advanced
-
Bandwidth
Mobile
Broadband
Ubiquitous
connectivity
xDSL-like
Fiber-like
Data rates
experience:
experience:
1 hr HD-movie 1 hr HD-movie
in 6 minutes
in 6 seconds
FIA, Athens, March 2014
20
20
15
15
Exabytes/Month
Billion Devices
Towards the 5G Era (2)
10
5
0
2013
2018
Year
5

10
5
0
2013
2018
Year
Mobile Data Traffic
Mobile Devices
Data by Cisco, Forecast 2013-2018
Evolution of communication paradigms
Key Communication Paradigms (1)
A traditional cell
(Macrocell)
BS
BS
BS
BS
Cellular
Cellular
links
links
MN
MN1 1
Picocell

MN
MN2 2
6
MN
MN3 3
MN
MN4 4
Multi-tier small cell networks
–

D2D
D2Dlink
link
Low(er)-power devices
Device-to-Device (D2D)
communications
Key Communication Paradigms (2)



20132018
# Devices: 1.5x
# Data traffic: 10x



7
Spectrum?
Traditional spectrum
availability is scarce
Bridging the
spectrum gap
with 5G
The Challenge


8
The fundamental challenge: Seamless
coexistence of autonomous devices that
share resources in such heterogeneous
networks
Our fundamental target: To design efficient
distributed radio resource management
(power control, channel access) schemes to
meet this challenge
Our Tools
1994 Nobel Econ.2014
P1
P2
Grand Bazaar, Istanbul
4P1
Power control
9
P2 4P1
P2?
Our Roadmap (1)


10
Competition for resources among players
=(non-cooperative) game theory
Players
Devices
Strategy
Which power?
When to transmit?
Utility
Ui(Pi,SINRi)
Our Roadmap (2)
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

11
Key question/solution
concept:
Has the game a Nash
Equilibrium (NE)?
How can we find it?
Is it unique? If not, which to
choose?
Is it (Pareto) efficient?
Incentives to end up at more
efficient operating points
Research Areas
& Key Contributions (1)
{1} Power Control and Bargaining in Scenarios with
Unsatisfied Autonomous Devices
–
–
–
The resulting NE is inefficient, even in small networks
Our distributed joint power control and bargaining scheme
increases the number of satisfied devices
More efficient-fair scheme than standard approaches
{2} Non-Cooperative Power Control in Two-Tier Small Cells
–
–
12
–
Contrary to typical formulations, we introduce different utility
functions for the types of nodes
Existence of a NE, derive conditions for uniqueness, fast
distributed convergence to the unique NE
Efficient coexistence is feasible in most scenarios
Research Areas
& Key Contributions (2)
{3} Channel Access Competition in Device-to-Device Networks
–
–
–
For linear/tree networks, we propose two distributed schemes
with different level of cooperation that converge fast to a NE
We analyze the structural properties of the NE
We highlight the differences from typical scheduling approaches
{4} Power Control and Bargaining under Licensed
Spectrum Sharing
–
–
13
Our joint power control and bargaining scheme outperforms both
the NE without bargaining and classical pricing schemes in terms
of revenue per operator and sum of revenues
A simple set of bargaining strategies maximizes the social
welfare for 2 operators with lower communication overhead than
pricing
A Classification of
Power Control Approaches
{2}
2G (Voice)
SIR-Based
[Zander 92]
3G/4G (Data)
SINR-Based
[F&M 93]
[Bambos 98]
{1},{2}
14
Utility without
cost part
[Saraydar 02]
{1},{4}
Utility with
cost part
[Alpcan 02]
{2}
V.G. Douros and G.C. Polyzos, “Review of Some Fundamental Approaches
for Power Control in Wireless Networks,” Elsevier Computer Communications,
vol. 34, no. 13, pp. 1580-1592, August 2011.
Power Control and Bargaining
under Licensed Spectrum Sharing
15
 V.G. Douros, S. Toumpis, and G.C. Polyzos, “Power Control and Bargaining
for Cellular Operator Revenue Increase under Licensed Spectrum
Sharing,” submitted for journal publication.
Motivation (1)
Deployments (mil.)
100
80
60
40
20
0
Small cell industry firsts
First launch
Sprint
September
Wireless (US)
2007
Metrocells
First enterprise
Verizon
January
Microcells
launch
Wireless (US)
2009
Picocells
First public
TOT
March
Femtocells
safety launch
(Thailand)
2011
First standardized Mosaic (US)
February
launch
2012
First LTE
SK Telecom
June
2011 2012
2013 2014 2015 (South
2016 Korea)
femtocell
2012
Year

16
December 2012: Data
FCCbyconsiders
3.5 GHz as the shared
Small Cell Forum
access small cells band
–
Currently used by U.S. Navy radar operations
Motivation (2)



Why shared?
Why small cells?
What about interference?
–

17
“We seek comment on […] mitigation techniques
[…] (3). The use of automatic power control […]” 
July 2014: Trials for licensed spectrum sharing
for complementary LTE-Advanced
Challenge and Contributions


The challenge: Ensure that wireless operators can
seamless coexist in licensed spectrum sharing
scenarios
Our contributions: Power control with bargaining for
improvement of operators’ revenues
–
–
18
Our joint power control and bargaining scheme
outperforms both the NE without bargaining and
classical pricing schemes in terms of revenue per
operator and sum of revenues
A simple set of bargaining strategies maximizes the
social welfare for the case of 2 operators with lower
communication overhead than pricing
System Model


N operators, 1 BS per
operator, 1 MN per BS
Each operator:
–
–
–
19
controls the power of
its BS
charges its MN per
round based on the QoS  Each device:
aims at maximizing its
– will not change operator
revenue per round
– downloads various files
– pays more for better QoS
without min./max. QoS
requirements
Game Formulation

A non-cooperative game formulation
Players
Strategy
Utility


20
BSs/Operators
Power Pi in
[Pmin,Pmax]
ci Blog(1+SIRi)
The game admits a unique Nash Equilibrium:
All BSs transmit at Pmax
Our work: Can we find a more efficient
operating point?
Analysis for N Operators (1)


21
Red makes a “take it or
leave it” offer to Black
“I give you o1,2 € to
reduce your power
M times”
Estimated
revenue
NE revenue
Analysis for N Operators (2)



22
Black accepts the offer iff:
Win-win scenario
Key question: Are there
cases that the maximum
offer that red can make is
larger than the minimum
offer that black should
receive?
Analysis for 2 Operators (1)
Theorem: Let
𝐺11
𝐺21
= 𝑞 and
𝐺22
𝐺12
= 𝑟 the ratios of the path
gain coefficient of the associated BS to the path gain
coefficient of the interfering BS.
𝑟
If M≥ max 1, , then 𝑜1,max ≥ 𝑜2,min
If M≥ max

23
𝑞
𝑞
1,
𝑟
, then 𝑜2,max ≥ 𝑜1,min
Good news: We can always find a better
operating point than the NE without bargaining
Analysis for 2 Operators (2)
Theorem: The maximum sum of revenues of the
operators corresponds to one of the
following operating points: A1=(P1, P2)=(Pmax, Pmin) or
A2=(P1, P2)=(Pmin, Pmax).

24
Better news: By asking for the maximum
power reduction, the operators will reach to an
agreement at either point A1 or point A2 and
they will maximize the social welfare
How to Pick a Good Offer?
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Full knowledge
“On the fly”
Partial knowledge
Distributed iterative
scheme
Start with max. offer
If the offer is
accepted, reduce it
Players
Strategy
BSs/Operators
Power Pi in
[Pmin,Pmax]
Utility
ciBlog(1+SIRi)
Numerical Examples (1)
BS2
BS1



MN2
OP1 offers
M=32
Step=1.15
𝐺11
 q=
𝐺21
26

r=
400
% Payoff Improvement
MN1
𝐺22
𝐺12
=1
=1
minimum
offer
Revenue
at the NE
BargainingA1
BargainingA2
300
200
All these points arelimit
more
efficient than the NE
100
0
0
2
4
6
Round
8
10
12
Maximum
BargainingA1(2):
Revenue of OP1 (OP2)
offer
when OP
1 makes offers
Numerical Examples (2)
BS2
14
MN2
MN1
BS1
Parameters
Spread factor L=4
Revenue
12
[Alpcan,02]
NE1
Pricing1
BargainingA1
10
BargainingA1>NE1
BargainingA1>Pricing1
in allin all
scenarios
8
Charging factor c=1
Pricing factor z=1.5
G11=0.5, G21=0.2
27
G12=0.05, G22=0.2
6
1
2
3
Scenarios
4
5
BargainingA1: Revenue of OP1
when OP1 makes offers
Numerical Examples (3)

BargainingA2>NE2 in all
scenarios
BargainingA2>Pricing2
in the first 3 scenarios
12
Revenue

14
[Alpcan,02]
NE2
Pricing2
BargainingA2
10
8
BS2
MN2
6
1
28
MN1
BS1
2
3
Scenarios
4
BargainingA2: Revenue of OP2
when OP1 makes offers
5
Numerical Examples (4)
Bargaining outperforms NE in all scenarios
14
NE1
NE2
Pricing1
Pricing2
BargainingA1
BargainingA2
10
12
Revenue
12
Revenue
14
8
6
1
29
10
NE1
NE2
Pricing1
Pricing2
BargainingB1
BargainingB2
OP2
makes
offers
8
2
3
Scenarios
4
5
6
1
2
3
Scenarios
Bargaining outperforms Pricing
4
5
Numerical Examples (5)Sum of Revenues

30
BargainingA/B
strictly outperforms
NE and Pricing in
terms of sum of
revenues
BargainingB
maximizes the
social welfare
19
18
Revenue

16
14
1
[Huang,06]
2
3
Scenarios
NE
BargainingA
BargainingB
Max Sum
Pricing
4
5
Sum of Revenues
Accumulated Results
Parameters
100
Best pricing factor z*=1.5
Gij={0.01,0.06,0.11,…,0.96}



31
Percentage of Scenarios
Charging factor c=1
80
60
MaxBargaining=MaxSum
MaxBargaining>Pricing
MinBargaining>Pricing
MaxBargaining=Pricing
~120,000 simulations
40
MaxBargaining strictly
outperforms Pricing in the
100%
20
vast majority of scenarios
Even MinBargaining strictly 0
4
8
16
32
64 128 256 512
outperforms Pricing in most
Spread Factor L
cases
MaxBargaining=max{BargainingA,BargainingB}
Qualitative Comparison (1)
Bargaining
Pricing
[Alpcan,02]
Social welfare
for 2 players
Yes
No guarantee
Each payoff >=
NE payoff
Yes
No
Additional path 1 path gain
gain knowledge
General
convergence
32
Yes
No
Only if
N-1<L
Qualitative Comparison (2)
33
Bargaining
MaxSum
[Huang,06]
Social welfare
for 2 players
Yes
Yes
Social welfare
for N players
Open issue
No
Additional path
gain knowledge 1 path gain
for 2 players
1 path gain +
1 pricing profile
Additional path
gain knowledge 1 path gain
for N players
N-1 path gains +
N-1 pricing
profiles
Agenda for Future Directions



N Operators
Minimum/maximum data rates
Coalitional game theory
–
–
–
–
34
How to share their revenues?
Shapley value, core
Nash Bargaining Solution
Communication overhead
Take-home Messages



35
Our work: Licensed spectrum sharing through
power control and bargaining
Appealing property: By combining power
control with bargaining, no player receives
lower payoff than its payoff at the NE
Highlight: Bargaining outperforms standard
pricing techniques
Channel Access Competition in
Device-to-Device Networks


36
V.G. Douros, S. Toumpis, and G.C. Polyzos, “Channel Access Competition in Linear
Multihop Device-to-Device Networks,” Proc. 10th International Wireless Communications and
Mobile Computing Conference (IWCMC), Nicosia, Cyprus, August 2014.
V.G. Douros, S. Toumpis, and G.C. Polyzos, “On the Nash Equilibria of Graphical Games for
Channel Access in Multihop Wireless Networks,” Proc. Wireless Evolution Beyond 2020
Workshop, in conjunction with IEEE Wireless Communications and Networking Conference
(WCNC), Istanbul, Turkey, April 2014.
Motivation (1)
37
[Asadi et al., IEEE Communications Surveys & Tutorials, 2014]
Motivation (2)



Proximal communication –
D2D scenarios
Realizing D2D ad hoc networks
Standards: WiFi Direct,
LTE Direct
–

Relay by smartphones, Japan trials
–
38
http://www.youtube.com/watch?v=
nffzJvcDgtc#t=79
[Qualcomm, July 2014]
–
[Nishiyama et al., IEEE Communications Magazine, 2014]
https://www.youtube.com/watch?v=JbxKPrPF6JQ
Challenge and Contributions


The challenge: Seamless coexistence of autonomous
devices that form a D2D network
Our work: Channel access in linear/tree D2D networks
–

Contributions:
–
–
–
39
When a node should send its data?
We propose two distributed schemes with different level of
cooperation that converge fast to a NE
We analyze the structural properties of the NE
We highlight the differences from typical scheduling
approaches
Problem Description (1)
1
2
3
4
5
6
1
2
3
4
5
6
Node 4 should neither transmit nor receive
 Each node
in this
linear receive
D2D network
either
Node
2 cannot
from node
1 transmits to
4 cannot
receive from node 5
one of its Node
neighbors
or waits
Nodes 2 and 4 cannot transmit to node 3
 Saturated unicast traffic, indifferent to which to transmit at


40
Node 3 transmits successfully to node 4 iff none of the
red transmissions take place
If node 3 decides to transmit to node 4, then none of the
green transmissions will succeed
Problem Description (2)


The problem: How can these
autonomous nodes avoid
collisions?
The (well-known) solution:
maximal scheduling…
–

41
is not enough/incentivecompatible 
We need to find equilibria!
1
2
3
1
2
3
1
2
3
Game Formulation
Players
Strategy
Payoff
Devices
{Wait,
Transmit to one
of the |D|
neighbors}
Success Tx: 1-c
Wait: 0
Fail Tx: -c
Success Tx > Wait > Fail Tx
c: a small positive constant
42



This is a special type of
game called graphical
game
Payoff depends on the
strategy of 2-hop
neighbors
We have also examined
another payoff model with
non-zero payoff for the
receiver
On the Nash Equilibria (1)



How can we find a Nash Equilibrium (NE)?
1
We do not look for a particular NE; any NE
t1 1
is acceptable
The (well-known) solution: Apply a best
t2 1
response scheme…
–

43
2
2
2
will not converge 
Our Scheme 1: A distributed iterative
randomized scheme, where nodes
exchange feedback in a 2-hop
neighborhood to decide upon their new
strategy
t3
1
2
On the Nash Equilibria (2)



44
Each node i has |Di|
neighbors and |Di|+1
strategies. Each
strategy is chosen
t1
with prob. 1/(|Di|+1)
t2
A successful
transmission is
repeated in the next
round
t3
Strategies that
cannot be chosen
increase the
probability of Wait
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
This is a NE! 
On the Nash Equilibria (3)
t1
1
2
3
4
5
t2
1
2
3
4
5


45
By studying the structure of the NE, we can identify
strategy subvectors that are guaranteed to be part of
a NE
We propose Scheme 2, a sophisticated scheme and
show that it converges monotonically to a NE
On the Nash Equilibria (4)
46
1
2
…
N-1
R
N
On the Nash Equilibria (5)
47
On the Nash Equilibria (6)



48
Scheme 2: A
successful
transmission is
t1
repeated iff it is
guaranteed that it will
t2
be part of a NE
vector
Nodes exchange
messages in a 3-hop t3
neighborhood
Is this faster than
Scheme 1?
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
4
5
Local NE
1
2
3
This is a NE! 
Performance Evaluation (1)


Scheme 2 outperforms Scheme 1
Even in big D2D networks, convergence to a NE is
40
NE with Scheme 2
very fast
NE with Scheme 1.
This holds in tree
Unbiased version
30
NE with Scheme 1.
D2D networks
Biased version
as well
(2/3 prob.
Number of Rounds

20
10
0
49
to transmit)
5 10 20
50 100 200
N: Number of Nodes (Log Scale)
500 1000
Performance Evaluation (2)

50
Good news:
Convergence to a NE
for Scheme 2 is ~
proportional to the
logarithm of the
number of nodes
of the network
Better news:
In <10 rounds, most
nodes converge to a
local NE
24
21
Number of Rounds

18
15
12
NE for all nodes
NE for 80% of
the nodes
7.65logN
7logN
8logN
9
6
3
0
5 10 20 50 100 200 500 1000
N: Number of Nodes (Log Scale)
Agenda for Future Directions


General D2D networks
Repeated non-cooperative games
–
–

Price of Anarchy, Price of Stability…
–
51
Enforce cooperation by repetition
Punish players that deviate from cooperation
Even in big perfect tree D2D networks:
Take-home Messages

Channel access for linear/tree D2D networks
using game theory
–
–

Highlight: Studying the structure of the NE is
very useful towards the design of efficient
schemes
–
–
52
NE with minimal cooperation
stronger notion than maximal scheduling
fast convergence
without spending much energy
Conclusions
53
Our Fundamental Target

Seamless coexistence of autonomous
devices that share resources in modern
heterogeneous networks
–
54
It can be done!… with known tools (power control,
channel access)
Our Work
{1} Power Control and Bargaining in Scenarios with
Unsatisfied Autonomous Devices
{2} Non-Cooperative Power Control in Two-Tier Small
Cell Networks
{3} Channel Access Competition in Device-to-Device
(D2D) Networks
{4} Power Control and Bargaining under Licensed
Spectrum Sharing (LSS)
55
Lessons Learnt
Power Control
{1}
{2}
X
X
Channel Access
{4} LSS
X
X
Efficient NE
X
X
No collisions
Inefficient NE
X
X
small revenues
Bargaining for more
efficient points
X
X
Theorems
Distributed schemes
56
{3} D2D
Different level of
cooperation
X
X
NE structure
X
NE structure
X
social welfare
Related Publications (1)
Journals

V.G. Douros, S. Toumpis, and G.C. Polyzos, “Power Control and Bargaining for
Cellular Operator Revenue Increase under Licensed Spectrum Sharing,”
submitted for journal publication.

V.G. Douros and G.C. Polyzos, “Review of Some Fundamental Approaches for
Power Control in Wireless Networks,” Elsevier Computer Communications, vol. 34,
no. 13, pp. 1580-1592, August 2011.
57
Conferences and Workshops

V.G. Douros, S. Toumpis, and G.C. Polyzos, “Channel Access Competition in Linear
Multihop Device-to-Device Networks,” Proc. 10th International Wireless
Communications and Mobile Computing Conference (IWCMC), Nicosia, Cyprus,
August 2014.

V.G. Douros, S. Toumpis, and G.C. Polyzos, “On the Nash Equilibria of Graphical
Games for Channel Access in Multihop Wireless Networks,” Proc. Wireless
Evolution Beyond 2020 Workshop, in conjunction with IEEE Wireless Communications
and Networking Conference (WCNC), Istanbul, Turkey, April 2014.
Related Publications (2)
Conferences and Workshops (continued)

V.G. Douros, S. Toumpis, and G.C. Polyzos, “Power Control under Best Response
Dynamics for Interference Mitigation in a Two-Tier Femtocell Network,” Proc. 8th
International Workshop on Resource Allocation and Cooperation in Wireless
Networks (RAWNET), Paderborn, Germany, May 2012.

V.G. Douros, G.C. Polyzos, and S. Toumpis, “Negotiation-Based Distributed
Power Control in Wireless Networks with Autonomous Nodes,” Proc. 73rd IEEE
Vehicular Technology Conference (VTC2011-Spring), Budapest, Hungary, May 2011.

V.G. Douros, G.C. Polyzos, and S. Toumpis “A Bargaining Approach to Power
Control in Networks of Autonomous Wireless Entities,” Proc. 8th ACM
International Symposium on Mobility Management and Wireless Access (MobiWAC),
Bodrum, Turkey, October 2010.
58
Acknowledgement

59
This research has been co-financed by the European
Union (European Social Fund-ESF) and Greek
national funds through the Operational Program
“Education and Lifelong Learning” of the National
Strategic Reference Framework (NSRF) – Research
Funding Program: Heracleitus II. Investing in
knowledge society through the European Social Fund.
This res
(Europea
the Oper
the Natio
 Ευχαριστώ! 
Vaggelis G. Douros
Mobile Multimedia Laboratory
Department of Informatics
School of Information Sciences and Technology
Athens University of Economics and Business
[email protected]
http://www.aueb.gr/users/douros/
60