1. 1 Overview of Research Areas and Contributions Tareq Y. Al-Naffouri Director Office of Cooperation with KAUST Assistant Professor Electrical Engineering

2. 2 Major Milestones PhD (Stanford), Jan. 2005 Assistant Prof. (KFUPM), May 2005 Two month summer visit (Cal Tech), Summer 2006 Fulbright Scholar (USC), Feb-Aug. 2008 Director of Cooperation with KAUST Office, Nov. 2008 Applied to Assoc. Prof., Jan. 10 th 2009

3. 3 ِ Research AreaCollaborations Adaptive signal processingTwo Brazilian Universities Statistical signal processingUSC Wireless communicationsU. of Texas at Dallas Information TheoryCal Tech Random Matrix TheorySUPELEC, France Seismic Signal ProcessingSchlumberger Geophysics Dept. (KFUPM) During PhD Research Areas and Collaborations After joining KFUPM

4. 4 An Analogy Spanish French German Training Useful Instruction

5. 5 This happens every semester Spanish French German Portuguese Russian Greek First Semester Second Semester

6. 6 How can we reduce the training over Spanish French German Portuguese Russian Greek

7. 7 Digital Communication Scenario Receiver needs to know the communication medium to recover the transmitted data reliably Some of transmission time sacrificed for the sake of training Training Useful Transmission 0 1 0 0 1 1 …

8. 8 Multiple Transmit Antennas With multiple antennas, we can transmit more data … But then more training is needed

9. 9 Problem Compounded by Mobility With mobility, we need to train more often as the medium keeps changing

10. 10 How to reduce the training overhead Utilize correlation/similarities between different channels, e.g. 1.Spacing between antennas 2.Speed of car The more similarities used the lower the training needed

11. 11 Figure shows error rate comparison between our algorithm and best algorithm known in literature ٍ State of the art performance ٍ Our algorithm Around 5 dB gain in performance

12. 12 Our contributions Identified and utilized all possible dimensions (seven) of similarity to reduce training overhead Training done in a transparent manner using a dynamic program Forward Backward Kalman Filter Research resulted in 1.2 Master Theses 2.2 IEEE papers 3.2 top European Journal papers 4.Book Chapter 5.Patent (pending)

13. 13 Combating Impulsive Noise in DSL Impulsive noise is a rare phenomenon But when it occurs it destroys the transmitted signal completely Caused by bursty disturbances Car ignition Telephone Network switching A vacuum cleaner

14. 14 Typical DSL Signal + Impulsive Noise Impulse prob: 3x10^-3 Impossible to differentiate signal from Imp. Noise Can not simply combat noise by clipping Result is a punctured signal

15. 15 Noise Estimation and Cancellation Time Domain Air Domain Immerse tire in water; Locate & eliminate puncture Water Domain Freq Domain Transmission Band (signal + noise) Guard Band Guard Band

16. 16 ٍ No impulsive noise Estimate/Remove Predict presence of noise Puncture Impulse probability Rate Around 35% increase in rate Effective Tech. for Combating Impulsive Noise

17. 17 Our Contributions Work used an emerging technique for identifying sparse phenomena from few observations Work done jointly with a Prof. at USC Research resulted in 1.M.S. Thesis (in progress) 2.2 IEEE papers (in progress) 3.Pending patent Working on establishing a research group for estimation of sparse phenomena

18. 18 Multi-user Information Theory Math Engl. K subjects N students M Professors Phys. Mgt. The larger the number of students, the more difficult it is to cover the material properly

19. 19 Broadcasting to Groups of Users Cartoon

20. 20 Assume there are K TV/Radio channels N users M Transmitters What is the information rate that can received reliably?

21. 21 How can we maintain a constant rate with increasing no. of users? Increase the number of Antennas How much should the number of antennas grow to maintain a constant rate? and you should not grow at any lower rate

22. 22 Our Contributions Study is first of its kind; researchers in past focused on independent users Study done jointly with researchers at Cal Tech Research resulted in 1.M.S. Thesis (in progress) 2.2 IEEE papers 3.European Journal paper

23. 23 Research Time-Line 20102009200820072006 Year SABIC Proj. Uni Proj SABIC Proj KACST Proj. Seismic Consrt Uni. Proj 1 Uni Proj 2 Junior Proj.Proj. Brzl. U. 2USC (Flbright) SUPELEC. Brzl. U. 1 SchlumbergerCal Tech U of TXCLBR 2 Grads out1 Grad out 1 Grad in 2 Grads out 2 Grads in Grads 4 IEEE Jrnl 1 Eurp Jrnl 3 IEEE Jrnl. 3 Eurp. Jrnl 3 Patents IEEE Jrnl. 1 Eurp Jrnl Book Ch. IEEE Jrnl. Jrnls

24. 24 Thank You

25. 25 How to Reduce the Training Period? Spanish French German Portuguese Russian Greek Use similarities and correlations between the various languages to reduce the training period