Deep Quantization for MIMO Channel Estimation

Matan Shohat, Georgee Tsintsadze, Nir Shlezinger, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Quantizers play a critical role in digital signal processing systems. In practice, quantizers are typically implemented using scalar analog-to-digital converters (ADCs), commonly utilizing a fixed uniform quantization rule which is ignorant of the task of the system. Recent works have shown that the performance of quantization systems utilizing scalar ADCs can be significantly improved by properly processing the analog signal prior to quantization. However, the implementation of such systems requires complete knowledge of the underlying model, which may not be available in practice. In this work we design task-oriented quantization systems with scalar ADCs using deep learning, focusing on the task of multiple-input multiple-output (MIMO) channel estimation. By utilizing deep learning, we construct a task-based quantization system, overcoming the need to explicitly recover the system model and to find the proper quantization rule for it. Our results indicate that the proposed method results in practical MIMO systems with scalar ADCs which are 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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3912-3916
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - 1 May 2019
Externally publishedYes
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Quantization
  • channel estimation.
  • deep learning

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