Serial Quantization for Sparse Time Sequences
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...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2022
|
| Online Access: | https://hdl.handle.net/1721.1/144017 |
| _version_ | 1826202993972740096 |
|---|---|
| author | Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C Medard, Muriel |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C Medard, Muriel |
| author_sort | Cohen, Alejandro |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:29:01Z |
| format | Article |
| id | mit-1721.1/144017 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:29:01Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1440172023-06-26T20:15:14Z Serial Quantization for Sparse Time Sequences Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C Medard, Muriel Massachusetts Institute of Technology. Research Laboratory of Electronics 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. 2022-07-25T15:41:46Z 2022-07-25T15:41:46Z 2021 2022-07-25T15:33:23Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144017 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. en 10.1109/TSP.2021.3083985 IEEE Transactions on Signal Processing Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Cohen, Alejandro Shlezinger, Nir Salamatian, Salman Eldar, Yonina C Medard, Muriel Serial Quantization for Sparse Time Sequences |
| title | Serial Quantization for Sparse Time Sequences |
| title_full | Serial Quantization for Sparse Time Sequences |
| title_fullStr | Serial Quantization for Sparse Time Sequences |
| title_full_unstemmed | Serial Quantization for Sparse Time Sequences |
| title_short | Serial Quantization for Sparse Time Sequences |
| title_sort | serial quantization for sparse time sequences |
| url | https://hdl.handle.net/1721.1/144017 |
| work_keys_str_mv | AT cohenalejandro serialquantizationforsparsetimesequences AT shlezingernir serialquantizationforsparsetimesequences AT salamatiansalman serialquantizationforsparsetimesequences AT eldaryoninac serialquantizationforsparsetimesequences AT medardmuriel serialquantizationforsparsetimesequences |