Programmable Radios: A Personal Viewpoint

1. Programmable Radios: A Personal Viewpoint Ashu Sabharwal Rice University Houston, TX

2. Congratulations to SoRa Team ! Rice University

3. Three Questions • Why did I get involved ? • Have I learnt anything ? • What is on my wishlist ? Rice University

4. Why Did I Get Involved ? • Major speed innovations occur at PHY/MAC layer • Coding (Convolutional, Viterbi, LDPC, Turbo) • MIMO • Opportunistic scheduling • Many more in pipeline • Cooperative coding, network codes, interference alignment… • Big departure from traditional networking • Need sanity checks… But do I really need it ? Rice University

5. Do I Really It ? • Wireless blessed with information/comm/coding theory • Many many success stories Rice University

6. Do I Really It ? • Wireless blessed with information/comm/coding theory • Many many success stories • But hear only success stories • Multiuser detection: 15+ yrs of work, never got deployed • Beamforming: 15+ yrs, maybe finally in WiMax PROOF-OF-CONCEPT CONCEPT Feedback loop too slow & largely broken ! Rice University

7. What do I Need ? • Quick and accurate answers • Physical, MAC layer tests • Full control of all variables Programmable, measurable and deployable Rice University

8. Hardware & Design Flows WARP WARPLab WARPMAC GNURadio SoRa USRP WARP SoRa Rice University

9. Delivering on Promises ? • Many publications for proof-of-concept • GNUradio • WARP • Very soon using SoRa • Fundamental shifts ? • Change in models ? • New dominant effects ? • New problem formulations ? Rice University

10. Case I: Quantized Beamforming (WiMax/802.11n) • Large expected gains from closed loop beam-forming Alamouti (theory) Beamforming (theory) Rice University

11. Robustness to Channel Model • Large expected gains from closed loop beam-forming • Error floor with a small model perturbation Beamforming (actual) Alamouti (actual) Rice University

12. Reason for Breakdown • Very sensitive to how long the channel remains constant • Breaks the equalizer and thus, whole PHY Beamforming (actual) Alamouti (actual) Rice University

13. New Model, Simple Fix • Re-model, accounting for channel change • New packet structure Rice University

14. New Model, Simple Fix • Beamforming advantage returns • Original model did not capture all dominant effects New Beamforming (actual) Rice University

15. New Foundations • More generally • Feedback errors and delay can cause havoc • Transmitter and receiver get mismatched • Nearly all theory predictions breaks down • Better models for physical layer models with fast feedback • New fundamental results (Aggarwal & Sabharwal’09) • Proof that too many feedback bits not useful • Often more than one feedback bit is a waste ! Rice University

16. Case II: Cooperative Coding • Physical layer, symbol time-scale cooperation • Use both routes simultaneously • Pool distributed resources of power/antennas Relay Source Receiver Rice University

17. Case II: Cooperative Coding • No system demonstration till date • Cannot wait 15 years to know its fate Relay Source Receiver Rice University

18. Case II: Cooperative Coding on WARP • Built with WARPLab • Allows fine-grained control of each piece • Systematic experiments to understand dominant effects 2x2x2 MIMO Relay Rice University

19. Case II: Cooperative Coding, First Results • Large gains with optimal processing • 6-9 dB over non-relay • 3-6 dB over simple • No RF or A/D Simple Optimal Wu, Amiri, Duarte, Cavallaro’09 Rice University

20. Case II: Cooperative Coding, First Results • With RF • Optimal degrades a lot • Simple is robust • Optimal very sensitive to perturbations • Why ? • A/D robs important bits • More antennas need more bits Simple Optimal Wu, Amiri, Duarte, Cavallaro’09 Rice University

21. Wish 1: Higher Quality Radios • Low-quality signals  no post-processing can save the day • WARP radios top of the line • But we need better to push the limits ! • Better dynamic range, lower noise floor and bigger A/D,D/A • Clean-slate research • Platforms should be an order of magnitude better • Then research can find new sweet spots Rice University

22. Case III: Local View in Networks • Why current info theory of networks of little use ? • Models miss an important component • Nodes only have local network information • Nodes mismatched in their knowledge Rice University

23. Theory of Distributed Decisions • Two elements (Aggarwal, Liu and Sabharwal’09) • A protocol abstraction which quantifies local view • Distributed protocols as channel codes • First info theory analysis with hidden nodes • Predicts the losses seen in practice • Losses are unavoidable Full view limit Capacity Local view Rice University

24. Wish 2: Cross-community Fertilization • Wireless is many communities • CE + EE + CS • Different languages: VHDL, MATLAB, C • WARP, WARPLab, WARP_MAC • Isolation and Integration • Isolated controlled experiments • Integration of concepts Rice University

25. Wish 2: Cross-community Fertilization • Much remains to be done • Tools remain hard to use • Little coherence across communities WARP WARPLab WARPMAC GNURadio SoRa USRP SoRa WARP Rice University

26. Wish 3: Hardware-“normalized” Results • How do you compare results from different testbeds • Different hardware • System bandwidth • Speed of processing • Some examples • EVM, spectral efficiency • Situation likely to get worse • Much remains to be done ! Rice University

27. Answers • Why did I get involved ? • Problems which are unsolved and relevant • Have I learnt anything ? • Yes, more to come ! • What is on my wish list ? • Higher quality radios • Cross-community fertilization • Hardware-normalized metrics Rice University

28. Exciting times, fun path ahead ! Questions ? WARP: http://warp.rice.edu Rice University