DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

Alejandro Cohen, Amit Solomon, Nir Shlezinger

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

1 Scopus citations

Abstract

Closing the gap between high data rates and low delay in real-time streaming applications is a major challenge in advanced communication systems. While adaptive network coding schemes have the potential of balancing the rate and the delay in real-time, they often rely on a prediction of the channel behavior. In practice, such a prediction is based on delayed feedbacks, making it difficult to acquire causality, particularly when the channel model is unknown. In this work, we propose a deep learning-based noise prediction (DeepNP) algorithm, which augments the recently proposed adaptive and causal random linear network coding scheme with a neural network that learns to carry out noise prediction from data. This neural augmentation is utilized to maximize the throughput while minimizing in-order delivery delay of the coding scheme, and operate in a channel-model-agnostic manner. We numerically show that performance can dramatically increase by the learned prediction of the channel noise rate, demonstrating that DeepNP gains up to a factor of four in mean and maximum delay and a factor of two in throughput compared with statistic-based network coding approaches.

Original languageEnglish
Title of host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherInstitute of Electrical and Electronics Engineers
Pages2690-2695
Number of pages6
ISBN (Electronic)9781665421591
DOIs
StatePublished - 3 Aug 2022
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: 26 Jun 20221 Jul 2022

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2022-June
ISSN (Print)2157-8095

Conference

Conference2022 IEEE International Symposium on Information Theory, ISIT 2022
Country/TerritoryFinland
CityEspoo
Period26/06/221/07/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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