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MOTA: Engineering an Operator Agnostic Mobile Service Supratim Deb, Kanthi Nagaraj, Vikram Srinivasan Bell Labs.

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Presentation on theme: "MOTA: Engineering an Operator Agnostic Mobile Service Supratim Deb, Kanthi Nagaraj, Vikram Srinivasan Bell Labs."— Presentation transcript:

1 MOTA: Engineering an Operator Agnostic Mobile Service Supratim Deb, Kanthi Nagaraj, Vikram Srinivasan Bell Labs

2 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

3 Wireless Service Today and the End User Perspective Takeaways: Spectrum shortage exacerbated by deployment practices Users demand choice Wireless service provider should depend on location, pricing and user preferences Src:

4 Challenges in Users Making Appropriate Choices? Option 1: Centralized entity makes choices. Operators unlikely to share network planning information. Option 2: Users 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:

5 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 CoreNet- 2 PGW 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

6 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? 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

7 Utilities and Proportional Fairness – The Framework for All Seasons! User utility Users Objective: Subject to: – Each application associates with only one interface – Each interface associates with only one operator. Weight of application a Rate of application a Price of application a Price sensitivity of application a Comment: Price for each operator is constant. Operators sets a single price per unit weight per technology across all cells

8 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 W j, and PHY rate of user u is r u and weight of his application is w a, 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 W j and its price p j.

9 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 weight 2.Total load User computes 1.Which operator to select for each technology 2.Which application goes to each technology Based on 1.Signaling information 2.Energy considerations 3.Application characteristics

10 Greedy User Algorithm Utility of associating application of weight w to base station j = w f(p j, W j, w) Utility of operator that offers maximum utility is G l Order application weights in increasing order w 1 <= w 2,… <= w n Assign applications in this order. Greedy Algorithm: Iterate over all applications In the rth step Assign application r to interface that maximizes

11 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.

12 Signaling for Mobile Users Question: What signaling information should the base station send that is useful from a users perspective? Answer: Something that will allow the user to compute her net utility when she associates with this operator and moves around. Question: Isnt this dependent on each users individual mobility pattern? Answer: Clearly yes. Hence convey only aggregate information based on average usage patterns. This could depend on time of day etc.

13 Signaling for Mobile Clients Base station tracks: = aggregate log(PHY rate) over the time spent in cell-k by user us application a, when it is initiated in cell j. = aggregate time spent in cell k, by users us application a initiated in cell-j For each application class, Base station k conveys: Cell j Cell k

14 User Utility and Algorithm Recall user utility in static case = w f(p j, W j, 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. 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.

15 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.

16 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

17 Performance Improvement as Fraction of Mobile Users is Varied Area Spectral Efficiency improves by 2.5X-4X

18 Performance Gain over Optimized Single Operator At least 60% gain over single operator with load balancing across technologies

19 Whats in it for the Operators? Price User Utility Traditional Model MOTA Model Operator incentive Simulations imply 20% incentive. Far more research required.

20 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.

21 Questions?

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