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Open AccessArticle

Deep Task-Based Quantization

1
School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
2
Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 7610001, Israel
*
Author to whom correspondence should be addressed.
This paper is an extended paper presented in the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Brighton, UK, on 12–17 May 2019.
Entropy 2021, 23(1), 104; https://doi.org/10.3390/e23010104
Received: 29 November 2020 / Revised: 11 January 2021 / Accepted: 12 January 2021 / Published: 13 January 2021
Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by processing the signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine their analog-to-digital mapping using deep learning tools. These mappings are designed to facilitate the task of recovering underlying information from the quantized signals. By using deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task. View Full-Text
Keywords: analog-to-digtal conversion; task-based quantization; deep learning analog-to-digtal conversion; task-based quantization; deep learning
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MDPI and ACS Style

Shlezinger, N.; Eldar, Y.C. Deep Task-Based Quantization. Entropy 2021, 23, 104.

AMA Style

Shlezinger N, Eldar YC. Deep Task-Based Quantization. Entropy. 2021; 23(1):104.

Chicago/Turabian Style

Shlezinger, Nir; Eldar, Yonina C. 2021. "Deep Task-Based Quantization" Entropy 23, no. 1: 104.

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