1. Stealing From an Ongoing Flow: Protocols and Prototypes Ashu Sabharwal Rice University EPFL (2007-08) Joint work with Scott Novich & Debashish Dash

2. Ashu SabharwalRice University Microsoft Summit 2008 Thanks to all the participants & Microsoft Big thanks to Ranveer for putting all this together

3. Ashu SabharwalRice University 7 Blind Mice

4. Ashu SabharwalRice University 7 Blind Mice

5. Ashu SabharwalRice University 7 Blind Mice Spear Fan Pillar Cliff Rope

6. Ashu SabharwalRice University 7 Blind Mice Cognitive Wireless

7. Ashu SabharwalRice University Cognitive Wireless Hype or Next Big Thing ? –Feasibility ? –Extent of Utility ? –Impact as big as we will like to believe ? Scientific questions –Relevant problem formulations –Platforms as technical demonstrators

8. Ashu SabharwalRice University Outline Testbeds/Platforms [7 minutes] –TFA –WARP Thought Experiment to a Demo [10 minutes] –Stealing from an ongoing flow –Formulation –Result & protocol

9. Ashu SabharwalRice University At-scale: TFA-Rice Mesh Network In low-income neighbourhood of Houston, Texas TFA Charter: To empower with technology Deployed: 4000+ real users over 4 Km 2

10. Ashu SabharwalRice University At-scale: TFA-Rice Mesh Network Current TFA speeds peak at 0.5 Mbps/user Goal: 4-10X gains At-speed: Use WARP for a clean-slate network WARP

11. Ashu SabharwalRice University Wireless open-Access Research Platform WARP –Programmable FPGA platform (Virtex IIPro, Virtex 4) –High-end MIMO (upto 4x4, 60-100 Mbps) –Frameworks for clean-slate designs

12. Ashu SabharwalRice University Wireless open-Access Research Platform Multiple Design Flows –WARP + Matlab = WARPLab (offline design) –Simulink + Sysgen = WARP_Phy + WARP_MAC (real-time) –Control & Management Plane = WARPnet (deployed networks)

13. Ashu SabharwalRice University WARP Users

14. Ashu SabharwalRice University UCSD UC Irvine USC Polytechnic Rutgers University of Waterloo University of Oulu Nile University RWTH Aachen University University of Klagenfurt UC Riverside UOIT UC Santa Cruz Drexel University UIUC Xilinx (3 sites) Nokia Beijing DRS Signal Solutions Spectrum Signal Processing Irvine Sensors ASTRI (Hong Kong) Communications Research Centre Motorola Bangalore Microsoft Research Beijing Toyota Info. Tech Ericsson Research WARP Users (by end of Summer08) Industry (11)Academia (15)

15. Ashu SabharwalRice University Applications Urban-scale mesh network deployments (TFA-Rice) –Camp & Knightly, Infocom08 MIMO : Sphere detection/decoding –3G-LTE, WiMax, 802.11n (Cavallaros group) PM protocols for low-power handsets –Liu and Zhong, Mobisys08 Cooperative communications –Random Access Cooperative Systems (Tech Report, Asilomar08) Cognitive wireless (today)

16. Ashu SabharwalRice University Purpose of a Testbed Verify a concept –Sanity check & feel good –Engineering approximation error Uncover surprises –Overhead multiplier effect observed in TFA –50X reduction in capacity due to routing packets –Need at-scale and at-speed systems for such discoveries Thought Experiment –Mantra is I will build –Forces you to start with the correct setup

17. Ashu SabharwalRice University Outline Testbeds/Platforms [7 minutes] –TFA –WARP Thought Experiment to a Demo [10 minutes] –Stealing from an ongoing flow –Formulation –Result & protocol

18. Ashu SabharwalRice University Two-Flow Network Objective: maximize rate R s Constraint: cannot reduce primarys rate Primary Secondary RpRp RsRs

19. Ashu SabharwalRice University Rate Region Since interfering links, tradeoff between their rates True for any choice of protocols Primary Secondary RpRp RsRs RpRp RsRs CpCp CsCs

20. Ashu SabharwalRice University Rate Region The whole region depends on topology –Topology = {h pp, h ss, h ps, h sp, … } If region is known, then rate R s is easy to find. h pp RpRp RsRs RpRp RsRs CpCp CsCs h ss h ps h sp

21. Ashu SabharwalRice University Key Issue: Lack of Knowledge Compound Network: The secondary does not know –the topology –R p How can it select the R s ? Primary Secondary RpRp RsRs RpRp R s ? CpCp CsCs

22. Ashu SabharwalRice University Without Help, Secondary Cannot Send Without any knowledge, max R s = 0 Solution = Cognition –Snoop to learn –What can one learn about this region ? RpRp RsRs

23. Ashu SabharwalRice University Information Content in Snooping Hear and decode all transmissions –Estimate primary rate, R p –eg. by listening to ACKs Estimates are never perfect –Overhearing over noisy wireless channels Primary Secondary Silent RpRp RsRs

24. Ashu SabharwalRice University Information Content in Snooping Not sufficient information to estimate the region Reason: Passive estimation –No feedback with primary Solution: Estimation by perturbation Primary Secondary Silent RpRp RsRs RpRp R s ?

25. Ashu SabharwalRice University Estimation by Perturbation Key requirement: Primary should be adapting its rate to network conditions (e.g. TCP) Feedback increases compound network capacity RpRp RsRs + Snoop R s

26. Ashu SabharwalRice University Estimation by Perturbation Inject packets at a small rate See if the primary is affected If not, increase rate till it does Then adjust RpRp RsRs Primary reacts here

27. Ashu SabharwalRice University Protocol Trajectory Slow start Adapt its rate to find optimal rate Tunable parameters, T transmit, T sense, R s Work in progress: characterize convergence rate R*sR*s Secondary rate time T transmit T sense

28. Ashu SabharwalRice University Demo on WARP Primary flow alternating between high and low data rates Secondary (estimation by perturbation) Secondary rate time RpRp R*sR*s RsRs

29. Ashu SabharwalRice University Demo on WARP Primary flow alternating between high and low data rates Secondary (estimation by perturbation) Loss = [R * s (t)-R s (t)]dt Secondary rate time RpRp R*sR*s RsRs

30. Ashu SabharwalRice University Lesson I: Starting Point Model as if you will build it –No network information is available –Everything has to be estimated Directly implementable without any rework –Prototype demo using WARP –Work by Scott Novich

31. Ashu SabharwalRice University Lesson II: Lack of Information Hard to steal from dumb devices (e.g. walkie talkies) –They do not react to increased interference Easier to steal from smart systems –Allows one to observe their behavior by perturbing them

32. Ashu SabharwalRice University Recap Prototyping useful at many levels –Discovering surprises (TFA Network) –Thought experiment (this talk) –Sanity check (demo later) Distributed cognitive wireless –Stealing from dumb devices not possible –Intelligently stealing from smart devices possible

33. Ashu SabharwalRice University Questions ? WARP: http://warp.rice.eduhttp://warp.rice.edu TFA: http://tfa.rice.eduhttp://tfa.rice.edu CMC: http://cmc.rice.eduhttp://cmc.rice.edu