Radio resource Management and Optimization

Seong-Lyun Kim Professor, School of EEE

Assoc Dean, College of Eng Director, Center for Flexible Radio

Yonsei Univ., Seoul, Korea

Viability of Reverse Pricing in Cellular Networks: A New Outlook on Resource Management

May 8, 20152015 KTH-Yonsei Joint Workshop on 5G research

Joint work with Sang Yeob Jung, Joon Soo Kim, and Jun Ki Hong

2

DB

Server

MAC

0.4 6 GHz

sparse sensingl

Licensed Spectrum

Open Access Spectrum

Licensed Flexible Service over Shared Spectrum:

user-deployed, low cost/complexity, ultra-dense cells

Pain “Sharing”

Protection

FR+

Cognitive Radio (conventional)

Center for Flexible Radio: Practically Limitless User Spectrum (PLUS)

3

Spectrum Res.: 50 kHz Time Res.: 200 ms Spatial Res.: 10 m x 10 m

>10dBm-125dBm

RadarTx

WiFiAP

LTEBS

DTVTower

Sensing Map

frequency

x-position

y-position

RadarTx-Rx

WiFiAP-Rx

LTEBS

DTVTower

LTERx

DTVRx

Opportunity Map 1단계: Spectrum Sensing Map • Receiver Mobility • Interference Prediction 2단계: DSA Opportunity Map

High-Resolution Low-Complexity Sensing at Yonsei

Sensing Map ➥ Opportunity Map

•DTVprimary

•LTE

•WiFi •Radar

•PS-LTE • LAA-LTE • D2D-LTE secondary

Low opportunity (0)

High opportunity (1)

Cloud

DB

ServerMAC

Cloud Sensing/OP Map DB Cloud MAC Interf. Mngmt.

Cloud DB/MAC at SNU

FBMC-based Flexible Duplex

Realtime DSA at Yonsei

Dynamic Spectrum Access via High-Resolution/Low-Complexity Spectrum Sensing

4

Part 1. Introduction

Trends of Mobile Data Traffic Growth

5

12

Nomadic Heavy User Problem (NHP)

1. Total traffic volume increase: 3.04 times (47.66 → 145.05 MB/day). 2. Aggregate walking and nomadic user traffic proportion increase: 2.64 times

(14.74 → 38.93%)3. Nomadic heavy user traffic proportion increase: 2.83 times (10.35 → 29.29%)

[Shim15] T. Shim, J. Park, S.-W. Ko, S.-L. Kim, B. H. Lee, and J. G. Choi, "Traffic Convexity Aware Cellular Networks: A Nomadic Heavy User Perspective," submitted to IEEE Wireless Communications (under revision).

6

11

Mobile Traffic Experimental Environments

Year 2012 2015

Radio access technology WCDMA / LTE WCDMA / LTE / LTE-A

Number of participants82

(male: 61, female: 21)70

(male: 54, female: 16)

Experimental period avg. 1.28 weeks avg. 1.13 weeks

Measurement applications LifeMap My Data Manager, Moves

User speedWalking: up to 10 km/h (avg. 2.58 km/h)

Nomadic: above 10 km/h (avg. 31.9 km/h)

[Shim15] T. Shim, J. Park, S.-W. Ko, S.-L. Kim, B. H. Lee, and J. G. Choi, "Traffic Convexity Aware Cellular Networks: A Nomadic Heavy User Perspective," submitted to IEEE Wireless Communications (under revision).

Visual Example of Our Experimental Data Collection

User Convexity: Convex-Shaped over Velocity

7

[Shim15] T. Shim, J. Park, S.-W. Ko, S.-L. Kim, B. H. Lee, and J. G. Choi, "Traffic Convexity Aware Cellular Networks: A Nomadic Heavy User Perspective," submitted to IEEE Wireless Communications (under revision).

13

Stationary Walking Vagabond0

10

20

30

40

50

60

70

80

90

100

Tra

ffic

Vo

lum

e (

MB

/da

y)

2015

2012

2.35

User Convexity: 3.04

Nomadic

Traffic Volume over User Speed: User Convexity

Def. User convexity: the traffic volume ratio of nomadic users to walking users

User convexity increase: 1.29 times (2.35 → 3.04)

Solution: Traffic Convexity Aware Cell Association

8

[Shim15] T. Shim, J. Park, S.-W. Ko, S.-L. Kim, B. H. Lee, and J. G. Choi, "Traffic Convexity Aware Cellular Networks: A Nomadic Heavy User Perspective," submitted to IEEE Wireless Communications (under revision).

Road to 5G: “How to Match Supply to Demand?”

9

Pricing: Prof. Frank Kelly’s Seminal Work [Kelly98]

• Effective tool for design, operation, and management in cellular networks

[Kelly98] F. Kelly, A. Maulloo, and D. Tan “Rate control for communication networks: Shadow price proportional fairness and stability,” J. Oper. Res. Soc., 1998.[Ha12] S. Ha, S. Sen, C. Joe-Wong, Y. Im, and M. Chiang, “TUBE: Time dependent pricing for mobile data,” Proc. ACM SIGCOMM, 2012.

Mismatch: “Theory” and “Practice” • High spatiotemporal user demand uncertainty

➡ Network demand: 10x in peak-hours vs. off-peak hours [Ha12]

“New congestion pricing framework”

Road to 5G: New Congestion Pricing Framework (1/3)

10

Smart Data Pricing (SDP) [Sen13]

• Broad set of ideas and principles beyond the traditional pricing (e.g., flat-rate or usage-based pricing)

➡ Edge devices as a part of network management

➡ Not just how much to charge, but also when and how to charge

[Sen13] S. Sen, C. Joe-Wong, S. Ha, and M. Chiang, Smart data pricing (sdp): Economic solutions to network congestion. SIGCOMM eBook on Recent Advances in Networking, 2013.

User participation in pricing

Traditional

Flat-rate Usage-based

Operator-driven SDP

Time dependent Pricing

User-driven SDP

Reverse pricing

Time-dependent

pricing

SDP 1. Operator-driven Time-Dependent Pricing (Forward Pricing) [Ha12] • Low prices in less-congested periods, shifting usage from peak to off-peak periods

Road to 5G: New Congestion Pricing Framework (2/3)

11[Ha12] S. Ha, S. Sen, C. Joe-Wong, Y. Im, and M. Chiang, “TUBE: Time dependent pricing for mobile data,” Proc. ACM SIGCOMM, 2012.

Fig. 1. Spectrum usage efficiency over time

➡ CLAIM: Operator still has to face or predict demand uncertainty

Road to 5G: New Congestion Pricing Framework (3/3)

12

SDP 2. Reverse Pricing [Jung15] • Common in travel industry (e.g., airlines and hotels) - priceline.com

• Concept: Data amount/rate purchased, rather than sold

[Jung15] S. Y. Jung and S.-L. Kim, “Viability of reverse pricing in cellular networks: A new outlook on resource management, http://arxiv.org/abs/1504.06395

Resource recommendation rule: 10 GB + ? GB

User 1

User 2

Available capacity: 20 GB

Operator

Price: 1 $/GB

Resource recommendation rule: 5 GB + ? GB

Hidden bid-acceptance threshold: ? $/GB

Name user 1’s price: ? $/GB

Name user 2’s price: ? $/GB

Demand: 10 GB

Payment: 10 $

Demand: 5 GB

Payment: 5 $

Objective

13

Q. How to design reverse pricing w/ forward pricing to achieve “triple-win” solutions?

1. Resource utilization efficiency

Realistic Issues - Incomplete Information1. Demand uncertainty: user demand expectation but not variance

2. User heterogeneity: willingness to pay, bidding profiles

2. Users’ total payoffs

3. Operator’s total revenue

14

Part 2. Teaser

15

Viability of Reverse Pricing (1/7)

Fig. 2. Toal user demand

Resource Utilization Efficiency

16

Viability of Reverse Pricing (2/7)

Users’ Total Payoffs

Fig. 3. Total user payoff

17

Viability of Reverse Pricing (3/7)

Fig. 4. Operator’s revenue

Operator’s Total Revenue

18

Q. Is proposed reverse pricing is Real, Implementable? ➡ We Architected & Prototyped a fully functional reverse pricing system via App. & Server ➡ Through experiment study, we will confirm viability of reverse pricing or compare theory and practice

Main

My Information

Name your own price

Results

Data usage pattern

Logout

‘Trafficbid'

App Server

‘AWS’

Android ver.

http://ramoyonsei.angelworks.co.kr/ta_admin/index.php?/home

Hello. This website is for reverse pricing

Bid Profiles

User Information

Settings

Can download to search “Trafficbid”

Viability of Reverse Pricing (4/7)

19

Viability of Reverse Pricing (5/7)

App. Prototype

20

Viability of Reverse Pricing (6/7)

Results

Number Time Winning bid Success / Fail

1 00:00 ~06:00 0.2 $/ MB O

2 06:00 ~ 12:00 0.3 $/ MB X

3 12:00 ~ 18:00 0.4 $/ MB O

4 18:00 ~ 24:00 0.7 $/ MB O

Data usage pattern

0

10

20

30

40

0 ~ 6 6 ~ 12 12 ~ 18 18 ~ 24

Dat

a am

ount

(MB)

Time (hour)

App. Prototype

21

Viability of Reverse Pricing (7/7)

Server• Set minimum participation price, winning bid, user-specific recommended data amount

22

Part 3. System Model

System Model (1/3)

23

Resource-Constrained & Time-Slotted Network• A total amount of limited resource (e.g., data amount, rate, bandwidth, etc)

• A single operator

• A set of users

• A set of time slots (e.g., peak, normal, off-peak demand slots)

Operator

user1 userI

…

Q

I = {1, · · · , I}

H = {1, · · · , H}

User

1’s

dem

and

0

25

50

75

100

Resource scheduling horizon

0 4 8 12 16 20 24

Fig. 5. Resource-constrained & time-slotted networkUs

er I’s

dem

and

0

25

50

75

100

Resource scheduling horizon

0 4 8 12 16 20 24

System Model (2/3)

24

ui(✓hi , s

hi , p

h) = ✓hi ln(1 + shi )� phshi

User’s Payoff Function• The payoff function for user at time slot

➡ Logarithmic utility function is commonly used to model the proportionally fair resource allocation [Basar02]

➡ is the maximum price per unit resource for user , implying changing necessities of resource over time

i h

time-varying willingness to pay resource ex-ante price

[Basar02] T. Basar and R. Srikant, “Revenue-maximizing pricing and capacity expansion in many-users regime,” Proc. IEEE INFOCOM, 2002.

✓hi i

System Model (3/3)

25

Interaction Between Operator and Users

Fig. 6. Timing of the game that characterizes the interaction between operator and users

Stage I. Pricing based on demand predictions

Stage II. Use or report resources as a price taker

Stage IV. Name your own price

Stage III. Resource allocation rule & Hidden bid-acceptance threshold

Analysis: Backward Induction

Timing of the game

Assumption

26

Part 4. Analysis

Stage IV. Name Your Own Price (1/2)

27

Users’ Participation Decisions in Stage IV• Operator publicly specify a minimum participation unit price below which is not sold

• User decides whether to take part in the pricing process, i.e.,

• From (1), define the set of potential users that would name strictly positive prices at time slot , i.e.,

0 phmin < phx

hi

i

✓

hi ln(1 + x

hi )� p

hminx

hi � ✓

hi ln(1 + s

hi )� p

hx

hi (1)

I+(phmin) =

(i 2 I : phmin

✓

hi ln

�(1 + x

hi )/(1 + s

hi )�+ p

hs

hi

x

hi

)

h

(2)

recommended resource by operator in Stage III

user1 userI

…useri

…Group 1: name own price! Group 2: Do not name own price!

Stage IV. Name Your Own Price (2/2)

28

Users’ Bidding Strategies in Stage IV• Hidden bid-acceptance threshold (price):⌧h ⇠ U [phmin, p

h]

max

phminbhi ph

ui(✓hi , x

hi , b

hi )P(b

hi � ⌧

h) + ui(✓

hi , s

hi , p

h)P(b

hi < ⌧

h), 8i 2 I+

(p

hmin), 8h 2 HP1: (3)

Proposition 1. For each user , the optimal solution for Problem P1 at each

time slot is given by

i 2 I

h 2 H

(4)

(1

2xhi

h✓

h

i

ln⇣

1+x

hi

1+s

hi

⌘+ s

h

i

p

h + x

h

i

p

h

min

i, 8i 2 I+(ph

min

),

0, 8i 2 I\I+(phmin

).bh

⇤

i =

Stage III. Resource Recommendation Rule & Hidden Bid-Acceptance Threshold (1/4)

29

Issue 1. Resource Allocation Rule • Q. What is fairness criterion among users for resource allocation?

➡Constraint 1. Demand Capacity

➡Constraint 2. Revenue via reverse pricing Revenue via forward pricing

User 1

User 2

Operator

10 GB

5 GB

20 GB

1 $/GB

�

‘user payment residual resource’

Stage III. Resource Recommendation Rule & Hidden Bid-Acceptance Threshold (1/4)

30

Issue 1. Resource Allocation Rule (Cont’d)• Q. What is fairness criterion among users for resource allocation?

➡ A. Proportional residual resource recommendation rule

/

User 1

User 2

Operator10 GB + (2/3) 5 GB

5 GB + (1/3) 5 GB

20 GB

⇥

⇥1 $/GB

Stage III. Resource Recommendation Rule & Hidden Bid-Acceptance Threshold (2/4)

31

Issue 2. Minimum Participation Unit Price• Q. How to publicly specify a minimum participation unit price?

➡A. At least the revenue earned by forward pricing

User 1

User 2

Operator10 GB + (2/3) 5 GB

5 GB + (1/3) 5 GB 20 GB

⇥

⇥

Lemma 1. The minimum participation unit price that the operator sets should satisfy the following condition

ph > phmin � phP

i2I shiQ

, 8h 2 H.

1 $/GB, 0.75 $/GB

(5)

Stage III. Resource Recommendation Rule & Hidden Bid-Acceptance Threshold (3/4)

32

Issue 2. Minimum Participation Unit Price (Cont’d)• Q. How to set minimum participation unit price for revenue maximization?

➡A. As a strategic variable, taking the following tradeoff into account

0

75

150

225

300

User 1 User 2 User 3 User 4

Low Highphmin phminbh

⇤

i

Fig. 7. The comparison of bidding profiles under different minimum participation unit prices

With high ,

do not name prices

phmin

With high ,

tend to name higher prices

phmin

Stage III. Resource Recommendation Rule & Hidden Bid-Acceptance Threshold (4/4)

33

Issue 3. Hidden Bid-Acceptance Threshold• Q. How to set hidden bid-acceptance threshold given a specific minimum participation unit price?

Proposition 2. For any minimum participation unit price subject to (5), the hidden bid acceptance threshold is given by

phmin

⌧h⇤= phmin (6)

➡A. Do not forgo potentially profitable trades via reverse pricing

Stage I and II. Forward Pricing and Users’ Desired Resources

34

Stage II. Users’ Desired Resources

P2: 8i 2 I, 8h 2 Hmax

shi

ui(✓hi , s

hi , p

h) = ✓hi ln(1 + shi )� phshi , (7)

Stage I. Forward Pricing based on Demand Predictions

max

p�0

X

h2H

X

i2IE✓h

i

�✓hi � ph

�P3:

s.t.X

i2I

✓✓hiph

� 1

◆ Q, 8h 2 H.

(8)

(9)

Proposition 3. Given , the optimal solution to Problem P3 is given by Q >

Pi2I ✓̃hi + ✏hi✓̃hI � ✏hI

� I

ph⇤=

Pi2I ✓̃hi + ✏hiQ+ I

, 8h 2 H. (10)

Demand uncertainty stemming from time-varying willingness to pay

35

Part 5. Numerical Results

36

Viability of Reverse Pricing (1/3)

Fig. 8. Toal user demand

Resource Utilization Efficiency • Forward pricing: still fluctuating residual capacity due to intrinsic demand uncertainty

• Reverse pricing w/ forward pricing: no capacity left over resource scheduling horizon (only in this example)

37

Viability of Reverse Pricing (2/3)

Users’ Total payoffs • The proposed pricing scheme always outperforms the forward pricing only

• Increased flexibility for pricing

Fig. 9. Total user payoff

➡ total user payoff

38

Viability of Reverse Pricing (3/3)

Fig. 10. Operator’s revenue

Operator’s Total Revenue • Perhaps surprisingly, operator can extract more revenue by reverse pricing

• Operator should re-examine the idea of involving users in pricing process

39

Goal of Maximizing Operator’s Revenue (1/3)

Fig. 11. Operator’s revenue under

Operator’s Total Revenue • At a particular time slot 1, should be chosen as 1.515, achieving maximum revenue gain (e.g., 28%)

• However, it is impossible to set it in reality due to some uncertainty of bidding profiles

p1min

p1min

40

Goal of Maximizing Operator’s Revenue (2/3)

Fig. 12. Total user payoff under

Users’ Total Payoffs • Total user payoff is non-increasing over

• This is due to the fact that users are induced to bid with higher prices as increases

p1min

p1min

p1min

41

Goal of Maximizing Operator’s Revenue (2/3)

Fig. 13. Resource utilization efficiency under

Resource Utilization Efficiency • Resource utilization efficiency is non-increasing over

• When , only some users name their own prices, decreasing resource utilization efficiency

• When , no users name their own prices, degenerating forward pricing only case.

p1min

p1min

1.515 < p1min < 1.519

1.519 p1min p1⇤= 1.5616

42

Part 6. Conclusion

43

The idea of involving users in pricing decisions seems counterproductive. But, it may be the time to ‘re-examine’ this assumption.

Q. How to design reverse pricing w/ forward pricing to achieve “triple-win” solutions?

1. Operator’s revenue - YES

2. Resource utilization efficiency - YES

3. Net utility summed over all users - YES

Concluding Remarks (1/2)

Radio resource Management and Optimization

Seong-Lyun Kim Professor, School of EEE

Assoc Dean, College of Eng Director, Center for Flexible Radio

Yonsei Univ., Seoul, Korea

Thank You

May 8, 20152015 KTH-Yonsei Joint Workshop on 5G research

Joint work with Sang Yeob Jung, Joon Soo Kim, and Jun Ki Hong

Reference

45

[Shim15] T. Shim, J. Park, S.-W. Ko, S.-L. Kim, B. H. Lee, and J. G. Choi, "Traffic Convexity Aware Cellular

Networks: A Nomadic Heavy User Perspective," submitted to IEEE Wireless Communications (under revision).

[Kelly98] F. Kelly, A. Maulloo, and D. Tan “Rate control for communication networks: Shadow price proportional

fairness and stability,” J. Oper. Res. Soc., 1998.

[Ha12] S. Ha, S. Sen, C. Joe-Wong, Y. Im, and M. Chiang, “TUBE: Time dependent pricing for mobile data,”

Proc. ACM SIGCOMM, 2012.

[Sen13] S. Sen, C. Joe-Wong, S. Ha, and M. Chiang, Smart data pricing (sdp): Economic solutions to network

congestion. SIGCOMM eBook on Recent Advances in Networking, 2013.

[Jung15] S. Y. Jung and S.-L. Kim, “Viability of reverse pricing in cellular networks: A new outlook on resource

management, http://arxiv.org/abs/1504.06395

[Basar02] T. Basar and R. Srikant, “Revenue-maximizing pricing and capacity expansion in many-users regime,”

Proc. IEEE INFOCOM, 2002.