Communications, Networking, and Signal Processing

1. Communications, Networking, and Signal Processing Anant Sahai March 10, 2007 Visit Day Open house this afternoon (2nd Floor Cory Hall) See space, meet students, see posters!

2. Wireless Research at UC Berkeley + Law, Economics

3. Wireless Systems of Tomorrow New technologies require a new way of thinking about critical resources

4. Here be dragons! • Information theory • Robust control and signal processing • Learning and distributed adaptation • Game theory and economics • And any other sharp enough blade … Our weapons:

5. Where do you fit in? • Grad school is not “Undergrad Part II” • Not just about learning new skills and practicing their application. • Great research is not a supercharged class project. • Come to Berkeley if you want to learn how to ask the right questions. • Research “taste” is what we aim to foster • One-on-one with your advisor • Ad-hoc collaborations with your fellow students • Seminars and student-led reading groups • High-risk/high-reward research

6. Samples of COM/NET/DSP Research • New approaches to 3D modeling and Video • New understanding of error correcting codes • New perspectives on spectrum sharing Much more information at open houses 1-3pm… • Video Lab: 307 Cory • WiFo + Connectivity Lab: 264 Cory

7. Video and Image Processing Lab • Theories, algorithms and applications of signals; image, video, and 3D data processing; • Director: Prof. Zakhor; founded in 1988 • Open house posters: 307 Cory: 1 – 3 pm • Web page: www-video.eecs.berkeley.edu • Current areas of activities: • Fast, automated, 3D modeling, visualization and rendering of large scale environments: indoor and outdoor • Wireless multimedia communication • Applications of image processing to IC processing: maskless lithography; optical proximity correction

8. Figure 1: An example of a residential area in downtown Berkeley which has been texture mapped with 8 airborne pictures on top of 3D geometry obtained via 1/2 meter resolution airborne lidar data

9. Open House: 264 Cory 1-3PM Web-page: wifo.eecs.berkeley.edu • Venkat Anantharam • Michael Gastpar • Kannan Ramchandran • Anant Sahai • David Tse • Martin Wainwright Focus on signal processing, information theory, and fundamental limits. Interface to economics and policy.

10. X: current frame X: current frame Lossless channel X-Y MPEG Encoder MPEG Decoder Y: Reference frame Y: Reference frame PRISM: Distributed Source Coding (DSC) based video coding(K. Ramchandran’s group)

11. X: current frame X: current frame Lossy channel f(X) DSC Decoder DSC Encoder Y’: corrupted reference frame PRISM: Distributed Source Coding (DSC) based video coding(K. Ramchandran’s group)

12. Investigation of Error Floors of Structured Low-Density Parity-Check Codes by Hardware Emulation(Zhengya Zhang, Lara Dolecek, B. Nikolic, V. Anantharam, and M. Wainwright) Region unreachable in software

13. Shannon meets Moore’s Law • Shannon said that we can get arbitrarily low probability of error with finite transmit power • Transistors are free, but power is not. • In short-range communication, this is not irrelevant. What is the analogy to the waterfall curve that includes decoding?

14. The need for guidance • Practical question: “What should we deploy in 2010, 2015, or 2020?” • Semiconductor side: roadmap + scaling • Gives an ability to plan and coordinate work across different levels. • No such connection on the comm. side. • Capacity calculations do not say anything about complexity and power. • Left to either guess, stick to tried/true approaches, or to invest a lot of engineering effort to even understand plausibility. • Need a path to connect to the roadmap.

15. An abstracted model for technology • Massively parallel ASIC implementation • Nodes have local memory • Might know a received sample • Might be responsible for a bit • Nodes have few neighbors • (a+1) maximum one-step away • Can send/get messages • Can relay for others • Nodes consume energy • e.g. 1 pJ per iteration • Nodes operate causally

16. Treat like a sensor network or distributed control problem. After a finite number of iterations, the node has only heard from a finite collection of neighbors. Allow any possible set of messages and computations within nodes Allow any possible code. Key idea: decoding neighborhoods

17. “Waterslide” curves bound total power Assuming 1pJ, a range of around 10-40 meters, ideal kT receiver noise, and 1/r2 path loss attenuation.

18. Spectrum: The Looming Future • Many heterogeneous wireless systems share the entire spectrum in a flexible and on-demand basis. • How to get from here to there?

19. A new hope: breaking the interference barrier(David Tse, Ayfer Ozgur and Olivier Leveque)

20. Spatial Spectrum-Sharing (Gastpar) • Each system must make sure it lives within a certain spatial interference footprint. (Requires spectrum sensing…) • Example: To the right of the boundary, the REDs must collectively satisfy a maximum interference constraint. • Leads to new capacity results (identify capacity “mirages”) and coding schemes

21. Spectrum: Where we are today • Most of the spectrum is allocated for specific uses and users. • But measurements show the allocated spectrum is vastly underutilized.

22. Semi-ideal case: perfect location information - Locations of TV transmitter and Cognitive radios are known. - Location of TV receivers is unknown Non-interference constraint translates into “Minimal No-talk” radius Primary System TV Primary Receiver TV set Minimal No Talk Radius

23. If we use SNR as a proxy for distance … - With worst case shadowing/multipath assumptions - Detector sensitivity must be set as low as -116 dBm (-98 -> -116) - Un-shadowed radios are also forced to shut up LOS channel Primary System TV Loss in Real estate ~ 100 km Shadowing Minimal No Talk Radius Detection Sensitivity = -116dBm

24. Noise + interference uncertainty Cabric et al Spurious tones, filter shapes, temperature changes – all impact our knowledge of noise. Calibration can reduce uncertainty but not eliminate it

25. Spectrum Sensing: Harder than it looks

26. How can we reclaim this lost real estate? - Cooperation … can budget less for shadowing since the chance that all radios are shadowed may be very low Primary System TV Min No Talk Radius No Talk radius with cooperation Detection Sensitivity = -116 -> -104 dBm What if independence assumptions are not true?

27. Need right metrics for safety and performance • Safety: no harmful interference to primary • Performance: recovered area for the secondary. • Fundamental incentive incompatibility in models • Secondary is tempted to be optimistic in optimizing performance. • The primary will always be more skeptical of the model.

28. FHI and WPAR: the right simple metrics FHI: worst-case prob of interference WPAR: normalized area recovered • Area closer to edge of primary likely to have more customers • Area far from edge likely to have another primary.

29. Cooperative Safety Is Fragile! Why should the primary trust our independence assumptions?

30. What if we knew the shadowing? - Then we could dynamically change our sensitivity … and regain lost real estate Detection Sensitivity = -98dBm Primary System TV Shadowing Minimal No Talk Radius Detection Sensitivity = -116dBm

31. Fundamental Sparsity Sutro Tower San Francisco 28 co-located transmitters GPS Satellites Many in the sky simultaneously Fremont Peak San Juan Battista 10 co-located transmitters

32. Cooperation between multiband radios Cooperation between multiband radios improves both PHI and PMO

33. Cooperation between multiband radios Can start with low PHI,large PMO point for a single radio. Primary just trusts that shadowing is correlated between bands.

34. Disneyland vs Yosemite • Owner controls access to preserve QoS for users • “Band-managers” own band and lease it out. • Monopoly • Public owns and sets guidelines for use • Unlicensed users are on their own • Competition “Spectrum tour guide” can coordinate users without band ownership

35. Potential Policy Alternatives

36. Cognitive Radio: Opportunistic Use of Spectrum • Reclaim underutilized spectrum • How complex must the radios be? • Can systems operate individually? • “If a radio system transmits in a band that nobody is listening to, does it cause interference?”

37. Theory’s role: help refine architecture • Start by studying idealized systems with perfect models. • Look for key bottlenecks for system-level performance and ways around them in the appropriate asymptotic limits. • Continue by modeling the impact of model uncertainty on the architecture. • If the stars have to align for the architecture to work, it is not worth implementing. • Identify the new bottlenecks introduced by uncertainty. • Shape the architecture so performance and safety depend only on solid assumptions.