Abstract
Data compression and compressed sensing algorithms exploit the structure present in a signal for its efficient representation and measurement, respectively. While most state-of-the-art data compression codes take advantage of complex patterns present in signals of interest, this is not the case in compressed sensing. This paper explores usage of efficient data compression codes in building compressed sensing recovery methods for stochastic processes. It is proved that for an i.i.d. process, compression-based compressed sensing achieves the fundamental limits in terms of the number of measurements. It is also proved that compressed sensing recovery methods built based on a family of universal compression codes yield a family of universal compressed sensing schemes.
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