Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 60, Issue 7, Pages 3496-3505Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2012.2194710
Keywords
Analog-to-digital conversion; compressed sensing; quantization
Categories
Funding
- NSF [CCF-0431150, CCF-0728867, CCF-0926127]
- DARPA/ONR [N66001-11-C-4092, N66001-11-1-4090]
- ONR [N00014-08-1-1067, N00014-08-1-1112, N00014-11-1-0714]
- AFOSR [FA9550-09-1-0432]
- ARO MURI [W911NF-07-1-0185, W911NF-09-1-0383]
- Texas Instruments Leadership University Program
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The recently introduced compressive sensing (CS) framework enables digital signal acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed, the number of CS measurements required for stable reconstruction is closer to the order of the signal complexity than the Nyquist rate. To date, the CS theory has focused on real-valued measurements, but in practice measurements are mapped to bits from a finite alphabet. Moreover, in many potential applications the total number of measurement bits is constrained, which suggests a tradeoff between the number of measurements and the number of bits per measurement. We study this situation in this paper and show that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement; in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement. A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement.
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