MOTA: Engineering an Operator Agnostic Mobile ServiceSupratim Deb, Kanthi Nagaraj, Vikram Srinivasan

Bell Labs

MOTA: Engineering an Operator Agnostic Mobile ServiceSupratim Deb, Kanthi Nagaraj, Vikram Srinivasan

Bell Labs

Mobile Data Explosion and Need for New Technological Innovations

FCC National Broadband Plan: 500 MHz of additional spectrum Technical and Business innovations that increase efficiency of spectrum utilization

Wireless Service Today and the End User Perspective

Takeaways: Spectrum shortage exacerbated by deployment practices Users demand choiceWireless service provider should depend on location, pricing and user preferences

Src: opensignalmaps.comSrc: opensignalmaps.com

Challenges in User’s Making Appropriate Choices?

Option 1: Centralized entity makes choices. Operators unlikely to share network planning

information.

Option 2: User’s use signal strength from different base stations This is insufficient and can result in poor user

experience. Additional signaling information needed.

Choice of network should depend on user mobility pattern Switching at fine time scales incurs huge overhead

in core network

Goal: Distributed decisions by each user Concise network signaling that accounts for

mobility Evolutionary over current standards.

Everyone joins O2

VF may be better choice

Src: opensignalmaps.comSrc: opensignalmaps.com

MOTA Service Model

Service Aggregator: New intermediary between users and operators

Responsible for maintaining customer relationships

Handles all control plane operations that cannot be handled by a single operator Tracking and paging

Billing and authentication

Seamless switching across operators at Layer 3

CoreNet-1

PGW

BTS

BTS

CoreNet-2

PGW

BTS

BTS

Service Aggregator

Cloud AAA

Server

Tracking &

Paging

Mobile IPv6

Anchor

Module for

Switching

Decisions

Network layer and above

MAC and lower layers

MIH Layer

MOTA Framework

What information should each operator maintain?

What aggregate information should be broadcast by each base station?

What information should each user maintain?

How should a client decide the following: What operator to associate with each interface

(2G, 3G, 4G)?

What applications to associate with each interface (Voice, video, data etc.)?

User mobility? Session duration?

User experience?

Network load?Network load?

Price?Price?Operator 1

Operator 2

Operator m

2G Interface

3G Interface

4G Interface

Application 1

Application 2

Application n

User behavior

Network experience

Battery status

Utilities and Proportional Fairness – The Framework for All Seasons!

User utility

User’s Objective:

Subject to:– Each application associates with only one interface– Each interface associates with only one operator.

aaaaaaa pRwpRU ln),(

Weight of application a Rate of application a

Price of application a

Price sensitivity of application a

)],([ aaAa

a pRUEMaximize

Comment: Price for each operator is constant. Operators sets a single price per unit

weight per technology across all cells

Signaling and Algorithm for Static Clients

Fact:

Proportional fair scheduling is typically used by most cellular technologies.

If total weight of applications associated with a base station j is Wj, and PHY rate of user u is ru

and weight of his application is wa, then aggregate rate user receives under proportional fair scheduling is:

Network Signaling for Static Users: Each base station only needs to transmit its aggregate load Wj and its price pj.

rj

aj r

W

wT

Recall

Operator 1

Operator 2

Operator m

2G Interface

3G Interface

4G Interface

Application 1

Application 2

Application n

Base station conveys 1. Price per unit weight2. Total load

User computes 1. Which operator to select for each technology2. Which application goes to each technology

Based on1. Signaling information2. Energy considerations3. Application characteristics

Greedy User Algorithm

Utility of associating application of weight w to base station j = w f(pj, Wj, w)

Utility of operator that offers maximum utility is Gl

Order application weights in increasing order w1 <= w2,… <= wn

Assign applications in this order.

Greedy Algorithm:

Iterate over all applications

In the rth step

Assign application r to interface that maximizes

),,(max)( wWpfwG jjoperators

l

)_()_(

)_()_(

weightcurrGweightcurr

wweightcurrGwweightcurr rr

Price of Anarchy – Global Efficiency versus Selfish Strategy

Theorem: Let r be vector of PHY data rates of all users. There exists a constant K, such that

Comment: Proportional fair scheduling at base stations ensures that local decisions are not very bad.

K1

)().( rGLOBALrKSELFISH

Signaling for Mobile Users

Question: What signaling information should the base station send that is useful from a user’s perspective?

Answer: Something that will allow the user to compute her net utility when she associates with this operator and moves around.

Question: Isn’t this dependent on each user’s individual mobility pattern?

Answer: Clearly yes. Hence convey only aggregate information based on average usage patterns. This could depend on time of day etc.

Signaling for Mobile Clients

Base station tracks:

= aggregate log(PHY rate) over the time spent in cell-k by user u’s application a, when it is initiated in cell j.

= aggregate time spent in cell k, by users u’s application a initiated in cell-j

For each application class, Base station k conveys:

)(, aR kj

Cell jCell j

Cell kCell k

)(, aT kj

k

kjkkj aTEWaRE )]([)ln()]([ ,,

User Utility and Algorithm

Recall user utility in static case = w f(pj, Wj, w)

In mobile case = /(application duration)

Assumption: Total load at base station much larger than individual weight of user applications

Can now apply standard Maximized Generalized Assignment algorithm E.g.: Local Greedy Search with ½ - factor approximation.

k

kjkkj aTEWaRE )]([)ln()]([ ,,

Static algo cannot be applied

Difficult to quantify price of anarchy. In mobile case, scenario is more dynamic. Similar to multiple agent learning. Difficult to prove strong guarantees.

Putting it together in practice

Implementation over Existing IEEE, IRTF and IETF proposals:

Use IEEE 802.21 for signaling

IRTF MPA framework for authentication and acquiring IP address and network resources.

Fast Handover in MIPv6 to simultaneously establish tunnel to gateway of new network and forward packets.

Gathering network state information:

Needs to be managed carefully depending on FDD versus TDD systems to minimize overhead.

Evaluation

Network Topology:

Cell tower location of a major operator in Indian city (5Km X 5Km area)

Clutter information along with RF tool used to generate RF map

We assume two operators share the same cell tower locations.

Each offers HSDPA and LTE

Application Models:

3 classes, voice, video and data

Generated according to guidelines for next generation mobile networks

User Mobility:

Manhattan and random waypoint

Performance Improvement as Fraction of Mobile Users is Varied

Area Spectral Efficiency improves by 2.5X-4X

Performance Gain over Optimized Single Operator

At least 60% gain over single operator with load balancing across technologies

What’s in it for the Operators?

PricePrice

User

Uti

lity

User

Uti

lity

Traditional ModelTraditional Model

MOTA ModelMOTA Model

Operator incentiveOperator incentive

Simulations imply 20% incentive. Far more research required.Simulations imply 20% incentive. Far more research required.

Reflections

Are there alternative simpler architectures possible that just exploit roaming agreements between operators?

How can this be combined with ideas of dynamic spectrum access? Do operators really need to swap spectrum at fine time scales?

Is operator signaling really required? Can end users learn appropriate association over time? A phone app that makes these

decisions for you.

Questions?