IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) 2019
DOI: 10.1109/infcomw.2019.8845224
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Practical Compression Methods for Quantized Compressed Sensing

Abstract: In order to save energy of low-power sensors in Internet of Things applications, minimizing the number of bits to compress and communicate real-valued sources with a predefined distortion becomes crucial. In such a lossy source coding context, we study rate-distortion (RD) performance of various single-sensor quantized compressed sensing (QCS) schemes for compressing sparse signals via quantized/encoded noisy linear measurements. The paper combines and refines the recent advances of QCS algorithm designs and t… Show more

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Cited by 5 publications
(15 citation statements)
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“…• A compress-and-estimate (CE) QCS method [20], [21] where 1) the encoder quantizes measurements y in (1) oblivious to x, and 2) the decoder estimates x from quantized measurementsỹ ∈ R M through a quadratically constrained polynomial-complexity basis pursuit (BP) problem 20 min.…”
Section: ) Baseline Qcs Methodsmentioning
confidence: 99%
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“…• A compress-and-estimate (CE) QCS method [20], [21] where 1) the encoder quantizes measurements y in (1) oblivious to x, and 2) the decoder estimates x from quantized measurementsỹ ∈ R M through a quadratically constrained polynomial-complexity basis pursuit (BP) problem 20 min.…”
Section: ) Baseline Qcs Methodsmentioning
confidence: 99%
“…• An estimate-and-compress (EC) QCS method, EC-VQ [20], [21], where 1) the encoder forms an MMSE estimate of x from y which is an exponentially complex task [57], and 2) quantizes the resulting estimate with a Lloydoptimized VQ. The EC strategy is known to be the optimal compression strategy for remote source coding [14], [30], while suffering from its high complexity.…”
Section: ) Baseline Qcs Methodsmentioning
confidence: 99%
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“…Sparsity in the sensed data allows to reduce the computations of simple sensor devices, and most importantly, to significantly reduce sensors' energy consumption for communicating the data to a fusion center [175,176,178]. One particular direction of great interest is so-called quantized CS [129,273,333,151,180,184,182,181] -a lossy compression setup where the CS measurements are converted into finite-rate bit sequences, and the aim is to design efficient quantization-aware CS reconstruction algorithms and analyze their ratedistortion performance. Application requirements may demand as low quantization rate as one bit per measurement sample, referred as 1-bit CS [152].…”
Section: Signal Sparsity In the Considered Applicationsmentioning
confidence: 99%