mota: engineering an operator agnostic mobile service
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MOTA: Engineering an Operator Agnostic Mobile Service Supratim 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 - PowerPoint PPT PresentationTRANSCRIPT
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?