SICNet—Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile Transceivers-Reference-Cited by-同舟云学术

SICNet—Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile Transceivers

Published:2022 Issue: Volume:3 Page:958-972
ISSN:2644-125X
Container-title:IEEE Open Journal of the Communications Society
language:
Short-container-title:IEEE Open J. Commun. Soc.

Author:

Ploder Oliver¹^ORCID,Auer Christina¹^ORCID,Motz Christian¹^ORCID,Paireder Thomas¹^ORCID,Lang Oliver²^ORCID,Huemer Mario²^ORCID

Affiliation:

1. Christian Doppler Laboratory for Digitally Assisted RF Transceivers for Future Mobile Communications, Institute of Signal Processing, Johannes Kepler University Linz, Linz, Austria

2. Institute of Signal Processing, Johannes Kepler University Linz, Linz, Austria

Funder

Austrian Federal Ministry for Digital and Economic Affairs

National Foundation for Research, Technology and Development

Christian Doppler Research Association

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/8782661/9702748/09792296.pdf?arnumber=9792296

Reference56 articles.

1. Support Vector Machines for Self-Interference Cancellation in Mobile Communication Transceivers

2. Kernel adaptive filters: A panacea for self-interference cancellation in mobile communication transceivers?;auer;Proc 17th Int Conf Comput -Aided Syst Theory (EUROCAST),2019

3. Mixed-Signal Based Enhanced Widely Linear Cancellation of Modulated Spur Interference in LTE-CA Transceivers

4. Digitally-intensive transceivers for future mobile communications—emerging trends and challenges

5. Active Digital Cancellation of Transmitter Induced Modulated Spur Interference in 4G LTE Carrier Aggregation Transceivers

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