<i>Retracted on July 26, 2022</i>
            : Open set recognition through unsupervised and class-distance learning

Draganov, Andrew; Brown, Carter N.; Mattei, Enrico; Dalton, Cass; Ranjit, Jaspreet

doi:10.1145/3395352.3402901

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Open set recognition (OSR) is a common phenomenon in which unknown classes not present in the training phase during learning are found in the testing phase [183], [184]. Also known as out-of-distribution (OOD) data [182], this issue can result in a lack of trust for deep learning models since the test set in real world applications is infinite, and even more so in wireless communications.…”

Section: F Issues With Open Set Recognition (Osr) or Out-of-distribut...mentioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands as well as other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as mitigation approaches based on expert systems. More recently, researchers have successfully explored artificial intelligence/machine learning enabled physical layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. In this paper, we address the knowledge gap in literature with respect to the state-of-the-art in deep learning-based interference suppression. Specifically, we review a wide range of techniques that have used deep learning to suppress interference by learning interference characteristics directly from data, rather than relying on expert systems. We provide a thorough technical discussion of the prominent deep learning algorithms that have been proposed in the literature and provide comparison and guidelines regarding their successful implementation in this application. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in this critical field.

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Section: F Issues With Open Set Recognition (Osr) or Out-of-distribut...mentioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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“…Open set recognition (OSR) is a common phenomenon in which unknown classes not present in the training phase during learning are found in the testing phase [179], [180]. Also known as out-of-distribution (OOD) data [178], this issue can result in a lack of trust for deep learning models since the test set in real world applications is infinite, and even more so in wireless communications.…”

Section: Issues With Open Set Recognition (Osr) or Out-of-distributio...mentioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Oyedare,

Shah,

Jakubisin

et al. 2021

Preprint

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In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as non-AI mitigation approaches (for example, adaptive filters). The key downside to non-AI techniques is the need for domain expertise in the extraction or exploitation of signal features such as cyclostationarity, bandwidth and modulation of the interfering signals. More recently, researchers have successfully explored AI/ML enabled physical (PHY) layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. The underlying idea of ML based approaches is to learn the interference or the interference characteristics from the data, thereby sidelining the need for domain expertise in suppressing the interference. In this paper, we review a wide range of techniques that have used deep learning to suppress interference. We provide comparison and guidelines for many different types of deep learning techniques in interference suppression. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in interference suppression.

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Novel device detection using RF fingerprints

Robinson

Kuzdeba

2021

2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

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Retracted on July 26, 2022 : Open set recognition through unsupervised and class-distance learning

Cited by 4 publications

References 11 publications

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Novel device detection using RF fingerprints

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