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© 2016 UZH, CSG@IFI
QoE Awareness for the Mobile Termination Rates Monopoly Liberation
Christos Tsiaras
Department of Informatics IFI, Communication Systems Group CSG, University of Zürich UZH
PhD Thesis Defense, Zurich, May 30, 2016
Introduction AQX Decompiling QoE AbaCUS MNO Selection Mechanism Summary
© 2016 UZH, CSG@IFI
Mobile Call Services Ecosystem
MvNO
Alice Bob
I want to call
MTR Service rate
Total cost of the call = Service rate + MTR CPP principle → Total cost
Caller’s network
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© 2016 UZH, CSG@IFI
Problem
MTR since the early days of mobile communication, are considered to be a “de-facto” monopoly
Could any of you connect me with Alice?
MvNO X Alice’s MvNO
I am the only network that can connect anyone with Alice!
! Only the MNO that Alice is connected with can reach her
! What about multiple SIM cards? – Not practical – Prior agreement is needed – Takes time – Is Apple SIM maybe a solution?
AT&T “bans” Apple SIM!
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© 2016 UZH, CSG@IFI
Research Questions Answered
! How can QoE be estimated for multiple and diverse variables?
! How to overcome MTR monopoly? – Charging for QoE – Switch between MvNOs
! Is an automatic and on-demand MvNO selection possible? – MvNO switching time and energy cost
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© 2016 UZH, CSG@IFI
AQX
! Formalizing QoE in axioms 1. Variables that affect QoE can be identified 2. Those variables can be characterized
• IVs - The more you have the better it is – E.g., quality
• AVs - The more you have the worst it is – E.g., price
3. Considering the service specifications it is possible to select the best and the worst values of each variable
4. There is an ideal/desired/expected/agreed value (x0) for each variable 5. The effect of each variable’s variation is identifiable
• Influence factors (m)
6. The importance (w) of each variable is identifiable
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© 2016 UZH, CSG@IFI
IV
Value [x ]
MOS[e
i(x
)]
0 x0 1
µ
e0
M
ei
x 0 − δ ′ x 0 + δ
e0 − ϵ ′
e0 + ϵ
Value [x ]
MOS[e
i(x
)]
0 x0
1µ
e0
M
m = 1m = 10−3
m = 103
QoE
IV variable
Influence factor (m)
Expected value (x0)
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© 2016 UZH, CSG@IFI
Influence Factors Calculation AV
Value [x]
MOS[e
a(x)]
0 x0
1µ
e0
M
x0 − δ′ x0 + δ
e0 − ϵ′
e0 + ϵ
ea
m- Area
m+ Area
m+ =
lnln 3− "ε
4ln 34
#
$
%%%
&
'
(((
ln x0 +δx0
m− =
lnln 3+ε
4ln 34
"
#
$$$
%
&
'''
ln x0 − (δx 0
QoE
IV variable
Influence factor (m)
Expected value (x0)
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© 2016 UZH, CSG@IFI
Generic QoE Illustration of an IV & AV
Value [x]
MOS[e
i∨a(x)]
h=
M−µ
xmin
x0
xmax
µ
e0
M
ei
ea
QoE
IV variable
Importance factor (wk)
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Poor quality for free
Excellent quality at a high price
This is where
your want your
customers to be
© 2016 UZH, CSG@IFI
The Design to Overcome the MTR Monopoly
! AbaCUS – Points to the MvNO to terminate a call – Triggers the MvNO switching
! Decision made by the Au2
– The auction provide incentives to MvNOs ! Auction parameters
– Expected QoE: MOS via AQX • QoS-C
– E.g., Sound quality
• TeR-C – E.g., MTR
! Automatic and on-demand MNO selection mechanism – Switches to the winning MvNO
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© 2016 UZH, CSG@IFI
QoS-C & TeR-C
Variable Type Classification Unit
QoS-C Sound Quality Technical IV Standard Definition (SD)
High Definition (HD)
Guaranteed Network Access Technical IV Yes No
TeR-C
MTR Economic AV Any Currency
Call Set-up Cost Economic AV -//-
Charging rate [time] Economic AV sec
Minimum Call Duration [time] Economic AV -//-
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© 2016 UZH, CSG@IFI
AbaCUS Algorithm
Caller MvNOs Preferences Announce TeR-Cs
No
Yes
Better option MvNO, QoS-C
Is there any better option?
Switch MvNO
Proceed without
any change
Start End
Input Condition
Output
Legend
Au2
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No better option exists
© 2016 UZH, CSG@IFI
MNO Selection Mechanism Implementation Steps
! The internal Android API provides a method to select the MNO
! Access the internal API – Custom Android framework – Modify the IDE access rule
! Sign the implemented mechanism with the system signature key
! Run the application – System user ID – Phone process
API
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© 2016 UZH, CSG@IFI
MNO Switching Mechanism Evaluation Set-up
! Count the MNO switching time and energy consumption – Between all possible MNO combinations – At different times across a day – In different locations – In different scenarios
• Stable in urban areas • Moving in a train (high handover demand)
=
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© 2016 UZH, CSG@IFI
MNO Switching Time / Pair of MNOs
Sw −> Su Sw −> Or Su −> Sw Su −> Or Or −> Sw Or −> Su Mean
0.5
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
MNO switching scenarios
MN
O s
witc
hing
tim
e [s
]
MNO selection time (stable)
MNO selection time (moving)
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= Or: Su: Sw:
4.3
s m
inim
um ti
me
need
ed
© 2016 UZH, CSG@IFI
MNO Switching Mechanism Power Consumption Evaluation
Process Power [W]
Talk 3G 0.7050
MNO Selection moving 0.6536
MNO Selection Stable 0.5406
Talk 2G 0.3333
Consumption 2G max talk time [h] 2G power [W] 3G max talk time [h] 3G power [W]
Voice service 18.33 0.3333 8.67 0.705
Stand-by 710.00 0.0086 610.00 0.010
Ptot =%Batteryused ⋅E(J )
texperiment (s)E = 6.11Wh ⋅3600s = 21996J
PMNO(stable) =0.14 ⋅21996J
5696s= 0.5406W
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© 2016 UZH, CSG@IFI
Summary
! QoE estimation while considering technical and economic variables simultaneously is possible – With the AQX model
! MTR monopoly obstacle can be overcome – By charging for QoE
! It is technically feasible to select on-demand the MvNO to terminate a call – Because it is possible in a “reasonable” MvNO switching time
and energy cost
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© 2016 UZH, CSG@IFI
Conclusions
! AQX is a generic QoE estimation model – Multidimensional – Non-service dependent – Supports diverse variables simultaneously
! Technology allows competition in the MTR market – MTR is not a “de-facto” monopoly anymore
! To enable competition in the MTR market an automatic and on-demand native implementation is needed – A prototype has been implemented and evaluated for the
Android platform
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© 2016 UZH, CSG@IFI
Future Work
! Incentive market analysis to reveal QoE-related variables
! Mechanism to protect dissatisfied end-users in case of QoE is not the expected
! Explore further auction types to increase infrastructure utilization and/or social wealth-fare
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© 2016 UZH, CSG@IFI
Thank you!
Q&A