Pilot assignment and channel estimation via deep neural network

Seunghwan Lee, Hyungyu Ju, Byonghyo Shim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In orthogonal frequency division multiplexing (OFDM) systems, channel estimation is by far the most important operation in the receiver to ensure the accurate detection and decoding. Over the years, pilot-aided channel estimation has been widely used for this purpose. In open-loop systems, since there is no feedback link between the transmitter and receiver, an approach based on the equi-spaced pilot assignment has been widely employed. In this paper, we propose a closed-loop non-uniform pilot allocation strategy based on deep neural network (DNN) technique. From the numerical evaluations, we show that the proposed autoencoder-based pilot allocation technique outperforms conventional approaches by a large margin, demonstrating its ability to learn the statistical characteristics of the wireless channel.

Original languageEnglish
Title of host publication2018 24th Asia-Pacific Conference on Communications, APCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages454-458
Number of pages5
ISBN (Electronic)9781538669280
DOIs
Publication statusPublished - 2019 Feb 1
Event24th Asia-Pacific Conference on Communications, APCC 2018 - Ningbo, China
Duration: 2018 Nov 122018 Nov 14

Publication series

Name2018 24th Asia-Pacific Conference on Communications, APCC 2018

Conference

Conference24th Asia-Pacific Conference on Communications, APCC 2018
CountryChina
CityNingbo
Period18/11/1218/11/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Communication

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  • Cite this

    Lee, S., Ju, H., & Shim, B. (2019). Pilot assignment and channel estimation via deep neural network. In 2018 24th Asia-Pacific Conference on Communications, APCC 2018 (pp. 454-458). [8633453] (2018 24th Asia-Pacific Conference on Communications, APCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCC.2018.8633453