SICNet—Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile Transceivers Article Swipe
Oliver Ploder
,
Christina Auer
,
Christian Motz
,
Thomas Paireder
,
Oliver Lang
,
Mario Huemer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2022.3181685
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2022.3181685
The limited transmitter-to-receiver stop-band isolation of the duplexers in long term evolution (LTE) and 5G/NR frequency division duplex transceivers induces leakage signals from the transmitter(s) (Tx) into the receiver(s) (Rx). These leakage signals are the root cause of a multitude of self-interference (SI) problems in the receiver path(s) diminishing a receiver’s sensitivity. Traditionally, these effects are counteracted by the use of various different SI cancellation (SIC) architectures which typically solely target one specific problem. In this paper, we propose two novel neural networks based architectures that can handle a variety of different SI effects without the need for a different architecture for each effect. We additionally show the suitability of the proposed architecture on SI effects occurring in in-band full duplex transceivers. Further, we introduce two novel low-cost training algorithms to enable online adaptation (as opposed to offline training currently proposed in literature). The combination of these two concepts is shown to not only beat existing algorithms in their cancellation performance, but also to provide sufficiently low computational complexity allowing on-chip implementations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2022.3181685
- OA Status
- gold
- Cited By
- 6
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285294087
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285294087Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ojcoms.2022.3181685Digital Object Identifier
- Title
-
SICNet—Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile TransceiversWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Oliver Ploder, Christina Auer, Christian Motz, Thomas Paireder, Oliver Lang, Mario HuemerList of authors in order
- Landing page
-
https://doi.org/10.1109/ojcoms.2022.3181685Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/ojcoms.2022.3181685Direct OA link when available
- Concepts
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Transceiver, Computer science, Interference (communication), Single antenna interference cancellation, Cellular network, Artificial neural network, Computer network, Telecommunications, Artificial intelligence, Wireless, Decoding methods, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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