Sharp, programmable, linear, integrated filters are enabling components for software defined and cognitive radio applications. However, they are difficult to realize: SAW and MEMS based filters are sharp and linear but not very programmable; active filters can be sharp and programmable but are not very linear; sampled charge domain filtering is sharp and programmable but the burden of the linearity is on the front end voltage-current converter. This talk describes an alternative approach that uses time-varying (as opposed to time-invariant) circuits to realize sharp, programmable, linear, integrated filters. The technique exploits sampling aliases to effectively realize very sharp, linear filtering prior to sampling. This talk will describe the basics of this time-varying circuit design approach and illustrates its application to radio front-ends and spectrum scanners. Measurement results from recent prototype integrated circuits will also be presented.
Dr. Sudhakar Pamarti is a professor of electrical engineering at the University of California, Los Angeles. He received the Bachelor of Technology degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur in 1995, and the Ph.D. degree in electrical engineering from the University of California, San Diego in 2003. Prior to joining UCLA, he has worked at Rambus Inc. (‘03-`05) and Hughes Software Systems (‘95-`97). Dr. Pamarti is a recipient of the National Science Foundation’s CAREER award for developing digital signal conditioning techniques to improve analog, mixed-signal, and radio frequency integrated circuits. Dr. Pamarti currently serves as an Associate Editor of the IEEE Transactions on Circuits and Systems I: Regular Papers and as a member of the CICC and ISSCC technical program committees