"Sampling and Frame Expansions for Ultra-Wideband and Adaptive-Band Signals"Casey, StephenOur talk develops a new approach to signal sampling, designed to deal with ultra-wide band (UWB) and adaptive frequency band (AFB) communication systems. These systems require either very high sampling or rapidly changing sampling rates. From a signal processing perspective, we have approached this problem by implementing an appropriate signal decomposition in the analog portion that provides parallel outputs for integrated digital conversion and processing. This naturally leads to an architecture with windowed time segmentation and parallel analog basis expansion. The method first windows the signal and then decomposes the signal into a basis via a continuous-time inner product operation, computing the basis coefficients in parallel. The windowing families are key, and we develop families that have variable partitioning length, variable roll-off and variable smoothness. We then show how these windowing families preserve orthogonality of any orthonormal systems between adjacent blocks, and use these to create bases in which do signal expansions in lapped transforms. We compute error bounds, demonstrating how to decrease error systematically by constructing more sophisticated basis systems. We also develop the method with a modified Gegenbauer system designed specifically for UWB signals. The overarching goal of the theory developed in this talk is to develop computable decompositions of time-frequency space. The idea is to come up with a way of non-uniformly tiling time and frequency so that if the signal has a burst of high-frequency information, we tile quickly and efficiently in time and broadly in frequency, whereas if the signal has a relatively low-frequency segment, we can tile broadly in time and efficiently in frequency. Computability is key; systems are designed so that they can be implemented in circuitry. The frame expansions greatly increase the computability and flexibility of these constructions. AFB expansions utilize Change Point Detection techniques to determine when to change the speed of the signal decompositions. This is joint work with Peter Balazs of ARI. |
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