TY - JOUR
T1 - The pros and cons of compressive sensing for wideband signal acquisition
T2 - Noise folding versus dynamic range
AU - Davenport, Mark A.
AU - Laska, Jason N.
AU - Treichler, John R.
AU - Baraniuk, Richard G.
N1 - Funding Information:
Manuscript received April 24, 2011; revised December 12, 2011 and April 26, 2012; accepted May 01, 2012. Date of publication May 30, 2012; date of current version August 07, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Raviv Raich. The work of M. A. Davenport was supported by the NSF Grant DMS-1004718. The work of J. N. Laska and R. G. Baraniuk was supported by the NSF Grants CCF-0431150, CCF-0728867, CCF-0926127, CNS-0435425, and CNS-0520280,, DARPA/ONR N66001-08-1-2065, N66001-11-1-4090, ONR N00014-07-1-0936, N00014-08-1-1067, N00014-08-1-1112, and N00014-08-1-1066, AFOSR FA9550-07-1-0301, and FA9550-09-1-0432, ARO MURI W911NF-07-1-0185, and W911NF-09-1-0383, and the Texas Instruments Leadership University Program. This work extends preliminary results presented in [1].
PY - 2012
Y1 - 2012
N2 - Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theory, commensurate reductions in the size, weight, power consumption, and/or monetary cost of both signal sensors and any associated communication links. This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment. We formulate the problem statement for such a receiver and establish a reasonable set of requirements that a receiver should meet to be practically useful. We then evaluate the performance of a CS-based receiver in two ways: via a theoretical analysis of its expected performance, with a particular emphasis on noise and dynamic range, and via simulations that compare the CS receiver against the performance expected from a conventional implementation. On the one hand, we show that CS-based systems that aim to reduce the number of acquired measurements are somewhat sensitive to signal noise, exhibiting a 3 dB SNR loss per octave of subsampling, which parallels the classic noise-folding phenomenon. On the other hand, we demonstrate that since they sample at a lower rate, CS-based systems can potentially attain a significantly larger dynamic range. Hence, we conclude that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings.
AB - Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theory, commensurate reductions in the size, weight, power consumption, and/or monetary cost of both signal sensors and any associated communication links. This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment. We formulate the problem statement for such a receiver and establish a reasonable set of requirements that a receiver should meet to be practically useful. We then evaluate the performance of a CS-based receiver in two ways: via a theoretical analysis of its expected performance, with a particular emphasis on noise and dynamic range, and via simulations that compare the CS receiver against the performance expected from a conventional implementation. On the one hand, we show that CS-based systems that aim to reduce the number of acquired measurements are somewhat sensitive to signal noise, exhibiting a 3 dB SNR loss per octave of subsampling, which parallels the classic noise-folding phenomenon. On the other hand, we demonstrate that since they sample at a lower rate, CS-based systems can potentially attain a significantly larger dynamic range. Hence, we conclude that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings.
KW - Analog-to-digital conversion
KW - compressive sensing
KW - dynamic range
KW - noise
KW - peak-to-average power ratio
KW - sampling
KW - wideband radio receivers
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U2 - 10.1109/TSP.2012.2201149
DO - 10.1109/TSP.2012.2201149
M3 - Article
AN - SCOPUS:84864478971
SN - 1053-587X
VL - 60
SP - 4628
EP - 4642
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 9
M1 - 6204356
ER -