TY - JOUR
T1 - Hardware-Limited Task-Based Quantization
AU - Shlezinger, Nir
AU - Eldar, Yonina C.
AU - Rodrigues, Miguel R.D.
N1 - Funding Information:
This work was supported in part by the European Unions Horizon 2020 research and innovation program under Grant 646804-ERC-COG-BNYQ, in part by the Israel Science Foundation under Grant 0100101, and in part by the Royal Society International Exchange scheme IE 160348.
Funding Information:
Manuscript received July 22, 2018; revised December 9, 2018 and July 28, 2019; accepted July 28, 2019. Date of publication August 19, 2019; date of current version September 12, 2019. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Youngchul Sung. This work was supported in part by the European Unions Horizon 2020 research and innovation program under Grant 646804-ERC-COG-BNYQ, in part by the Israel Science Foundation under Grant 0100101, and in part by the Royal Society International Exchange scheme IE 160348. This paper was presented in part at the 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Cannes, France, July 2–5, 2019. (Corresponding author: Nir Shlezinger.) N. Shlezinger and Y. C. Eldar are with the Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 7610001, Israel (e-mail: nirshlezinger1@gmail.com; yonina@weizmann.ac.il).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2019/10/15
Y1 - 2019/10/15
N2 - Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.
AB - Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.
KW - Quantization
KW - analog-to-digital conversion
UR - http://www.scopus.com/inward/record.url?scp=85077738006&partnerID=8YFLogxK
U2 - 10.1109/TSP.2019.2935864
DO - 10.1109/TSP.2019.2935864
M3 - Article
AN - SCOPUS:85077738006
VL - 67
SP - 5223
EP - 5238
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 20
M1 - 8805173
ER -