Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders

Chi Lun Lin, Kate Ching Ju Lin, Chi Cheng Lee, Yu Tsao

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

Abstract

Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby ZigBee devices, LTE-U UEs or even microwave ovens could emit RF signals over the frequency partially overlapping with the WiFi band. To combat such CTI, existing solutions have proposed several signal processing algorithms for error recovery or interference cancellation. However, most of those approaches need knowledge about the physical layer structure of CTI, which cannot be applied to denoise the unstructured interference from unknown electronics, e.g., microwave ovens. To overcome this deficiency, we present a CTI suppression framework based on Denoising AutoEncoder (DAE). The DAE is developed to learn the patterns of interference with unknown structures and passively suppress CTI with the zero cost. To avoid the expansive human cost of data collection, we propose a systematic way to synthesize corrupted WiFi signals for model training. Our experiments verify that the model trained with synthesized data can effectively reconstruct real corrupted WiFi signals and improve the decoding success probability.

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728182988
DOIs
StatePublished - Dec 2020
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan
Duration: 7 Dec 202011 Dec 2020

Publication series

Name2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
CountryTaiwan
CityVirtual, Taipei
Period7/12/2011/12/20

Keywords

  • autoencoder
  • cross-technology interference
  • denoising
  • interference suppression

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