Distributed Quantization for Sparse Time Sequences
Name
1910.09519.pdf
Description
Submitted version
Size
677.34 KB
Format
Adobe PDF
Checksum (MD5)
690ec3c173a40ec049baf7c25933dfe9
Author(s)
Cohen, Alejandro
Shlezinger, Nir
Salamatian, Salman
Eldar, Yonina C
Medard, Muriel
Date Issued
2020
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2020 IEEE. Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of analog signals are acquired individually and processed jointly. Such setups are referred to as distributed quantization. In this work we propose a distributed quantization scheme for representing a set of sparse time sequences acquired using conventional scalar ADCs. Our approach utilizes tools from secure group testing theory to exploit the sparse nature of the acquired analog signals, obtaining a compact and accurate representation while operating in a distributed fashion. We then show how our technique can be implemented when the quantized signals are transmitted over a multihop communication network providing a low-complexity network policy for routing and signal recovery. Our numerical evaluations demonstrate that the proposed scheme notably outperforms conventional methods based on the combination of quantization and compressed sensing tools.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICASSP40776.2020.9054339
