Serial Quantization for Sparse Time Sequences
Name
1907.01691.pdf
Description
Accepted version
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891.71 KB
Format
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Checksum (MD5)
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Author(s)
Cohen, Alejandro
Shlezinger, Nir
Salamatian, Salman
Eldar, Yonina C
Medard, Muriel
Date Issued
2021
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cohen, Alejandro, Shlezinger, Nir, Salamatian, Salman, Eldar, Yonina C and Medard, Muriel. 2021. "Serial Quantization for Sparse Time Sequences." IEEE Transactions on Signal Processing, 69.
Version
Author's final manuscript
Abstract
Sparse signals are encountered in a broad range of applications. In order to
process these signals using digital hardware, they must be first sampled and
quantized using an analog-to-digital convertor (ADC), which typically operates
in a serial scalar manner. In this work, we propose a method for serial
quantization of sparse time sequences (SQuaTS) inspired by group testing
theory, which is designed to reliably and accurately quantize sparse signals
acquired in a sequential manner using serial scalar ADCs. Unlike previously
proposed approaches which combine quantization and compressed sensing (CS), our
SQuaTS scheme updates its representation on each incoming analog sample and
does not require the complete signal to be observed and stored in analog prior
to quantization. We characterize the asymptotic tradeoff between accuracy and
quantization rate of SQuaTS as well as its computational burden. We also
propose a variation of SQuaTS, which trades rate for computational efficiency.
Next, we show how SQuaTS can be naturally extended to distributed quantization
scenarios, where a set of jointly sparse time sequences are acquired
individually and processed jointly. Our numerical results demonstrate that
SQuaTS is capable of achieving substantially improved representation accuracy
over previous CS-based schemes without requiring the complete set of analog
signal samples to be observed prior to its quantization, making it an
attractive approach for acquiring sparse time sequences.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
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DOI of Published Version
https://doi.org/10.1109/TSP.2021.3083985
