Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders

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 s...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE Global Communications Conference (Online) S. 1 - 6
Hauptverfasser: Lin, Chi-Lun, Lin, Kate Ching-Ju, Lee, Chi-Cheng, Tsao, Yu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2020
Schlagworte:
autoencoder
cross-technology interference
denoising
Gallium nitride
Generators
Interference
interference suppression
Microwave imaging
Noise reduction
Training
Wireless fidelity
ISSN:2576-6813
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
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.
AbstractList 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.
Author Lee, Chi-Cheng
Lin, Kate Ching-Ju
Tsao, Yu
Lin, Chi-Lun
Author_xml – sequence: 1
  givenname: Chi-Lun
  surname: Lin
  fullname: Lin, Chi-Lun
  email: lin0630.cs05@nctu.edu.tw
  organization: National Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan
– sequence: 2
  givenname: Kate Ching-Ju
  surname: Lin
  fullname: Lin, Kate Ching-Ju
  email: katelin@cs.nctu.edu.tw
  organization: National Chiao Tung University,Department of Computer Science,Hsinchu,Taiwan
– sequence: 3
  givenname: Chi-Cheng
  surname: Lee
  fullname: Lee, Chi-Cheng
  email: changlee@mail.iis.sinica.edu.tw
  organization: Research Center for Information Technology Innovation, Academia Sinica,Taipei,Taiwan
– sequence: 4
  givenname: Yu
  surname: Tsao
  fullname: Tsao, Yu
  email: yu.tsao@citi.sinica.edu.tw
  organization: Research Center for Information Technology Innovation, Academia Sinica,Taipei,Taiwan
BookMark eNotkMFOwkAURUejiYh8gZvGffHNm7a8WWIFJClhgwtXZNp54Jg6YzrFhL8XldVNzs25i3srrnzwLMSDhLGUoB8X1fppVq5XGQLgGAFhrBWiyuBCjPSE5ARJEmqiSzHAfFKkBUl1I0YxfsBJyaVCDQPxVnYhxnTDzbsPbdgfk6Xvudtxx77hZOV6tze9Cz55jc7vk_mhbY9JGfx3aA-_3LTJM_vg_trpoQ8nL1ju4p243pk28uicQ7GZzzblS1qtF8tyWqUOQfUp1jkXRltbKIK6sXZnjSHb1FI3BI3OmIgAa2AwKIl0XeQ6z2sENqrI1FDc_886Zt5-de7TdMft-Qv1A5-4WE8
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/GLOBECOM42002.2020.9322340
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9781728182988
1728182980
EISSN 2576-6813
EndPage 6
ExternalDocumentID 9322340
Genre orig-research
GroupedDBID 6IE
6IH
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IJVOP
M43
OCL
RIE
RIL
RIO
ID FETCH-LOGICAL-i203t-2b5e6a9dd6380bcddfdaa8dcb19c80c94e88802b0e0a21889b65955b20ea3643
IEDL.DBID RIE
ISICitedReferencesCount 5
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000668970501131&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 05:43:27 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-2b5e6a9dd6380bcddfdaa8dcb19c80c94e88802b0e0a21889b65955b20ea3643
PageCount 6
ParticipantIDs ieee_primary_9322340
PublicationCentury 2000
PublicationDate 2020-Dec.
PublicationDateYYYYMMDD 2020-12-01
PublicationDate_xml – month: 12
  year: 2020
  text: 2020-Dec.
PublicationDecade 2020
PublicationTitle IEEE Global Communications Conference (Online)
PublicationTitleAbbrev GLOCOM
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0002513290
Score 1.7759712
Snippet Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms autoencoder
cross-technology interference
denoising
Gallium nitride
Generators
Interference
interference suppression
Microwave imaging
Noise reduction
Training
Wireless fidelity
Title Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders
URI https://ieeexplore.ieee.org/document/9322340
WOSCitedRecordID wos000668970501131&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZKxQALjxbxVgZG3Dp2Xh6htDD0NVSoTJUfFykSSlBJK_HvsZ20BYmFzTorp8jn6PNdvvuM0J0JasQUl9j3ZYqDgCgsgAJmIZMqYikPXPvY6zAej5P5nE8b6H7bCwMAjnwGHTt0__J1oVa2VNY1Zw3KApOg78VxVPVqbespBqcZ5RtdUZ_w7vNw8tjvTUaB5SGYTJCSTu3g100qDkgGR_97hWPU3nXkedMt1pygBuSn6PCHmGALvfUs4OFdrdxz1b7N06OsUtMocs_RBDybfH55xv-63n3i3XuCvMjc7MOqLKzGpeU5t9Fs0J_1XnB9cQLOKGElpjKESHCtzcdFpNI61UIkWkmfq4QoHoDJewmVBIgwEJ9waVUFQ0kJCGaOKGeomRc5nCNPx2EsGYRSKGOnxnEoIqWp0oIkqR9foJZdo8VHJY2xqJfn8m_zFTqwYajYINeoWS5XcIP21brMPpe3Lp7f4Kai5Q
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFA5DBfXFyybe7YOPZkuTtGsedW5O7C4PQ-bTyK1QkFZmN_Dfm6TdpuCLb-GEHkpOypdz-p0vANyaoIZEMgF9XySQUiQh11hDEhAhQ5Iw6trHXuP2cBhNp2xcA3frXhittSOf6aYdun_5KpcLWyprmbMGJtQk6NsBpRiV3VrriopBaoLZSlnUR6z1FI8eup3RgFomgskFMWpWLn7dpeKgpHfwv5c4BI1NT543XqPNEajp7Bjs_5ATrIO3joU8uKmWe67et3p6kJZ6GnnmOaKAZ9PPL8_4X1b7j797jzrLUzd7vyhyq3Jpmc4NMOl1J50-rK5OgClGpIBYBDrkTCnzeSEhlUoU55GSwmcyQpJRbTJfhAXSiBuQj5iwuoKBwEhzYg4pJ2AryzN9CjzVDtqC6EBwaezYOA54KBWWiqMo8dtnoG7XaPZRimPMquU5_9t8A3b7k0E8i5-HLxdgz4ak5IZcgq1ivtBXYEcui_Rzfu1i-w2GeaYs
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+Global+Communications+Conference+%28Online%29&rft.atitle=Cross-Technology+Interference+Mitigation+Using+Fully+Convolutional+Denoising+Autoencoders&rft.au=Lin%2C+Chi-Lun&rft.au=Lin%2C+Kate+Ching-Ju&rft.au=Lee%2C+Chi-Cheng&rft.au=Tsao%2C+Yu&rft.date=2020-12-01&rft.pub=IEEE&rft.eissn=2576-6813&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FGLOBECOM42002.2020.9322340&rft.externalDocID=9322340