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A Real-Time Deep Learning OFDM Receiver

Published: 27 December 2021 Publication History

Abstract

Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.

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  • (2023)Data Augmentation for Deep ReceiversIEEE Transactions on Wireless Communications10.1109/TWC.2023.326178222:11(8259-8274)Online publication date: 30-Mar-2023
  • (2023)Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on ChipIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.327455531:7(1026-1038)Online publication date: 1-Jul-2023
  • (2023)A Moving Target Defense Approach for the Distributed Dynamic Network2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00076(336-343)Online publication date: 21-Dec-2023

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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems
ACM Transactions on Reconfigurable Technology and Systems  Volume 15, Issue 3
September 2022
353 pages
ISSN:1936-7406
EISSN:1936-7414
DOI:10.1145/3508070
  • Editor:
  • Deming Chen
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2021
Accepted: 01 October 2021
Revised: 01 September 2021
Received: 01 June 2021
Published in TRETS Volume 15, Issue 3

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Author Tags

  1. Neural networks
  2. OFDM
  3. FPGA
  4. physical layer processing
  5. machine learning acceleration
  6. real time

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  • Research-article
  • Refereed

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  • Engineering and Physical Sciences Research Council of the United Kingdom and Alpha Data Parallel Systems Ltd.

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Cited By

View all
  • (2023)Data Augmentation for Deep ReceiversIEEE Transactions on Wireless Communications10.1109/TWC.2023.326178222:11(8259-8274)Online publication date: 30-Mar-2023
  • (2023)Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on ChipIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.327455531:7(1026-1038)Online publication date: 1-Jul-2023
  • (2023)A Moving Target Defense Approach for the Distributed Dynamic Network2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00076(336-343)Online publication date: 21-Dec-2023

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