Blind separation of voice modulated single-side-band using the multi-target variable modulus algorithm

Fite, John D.; Bruzzone, S.; Agee, B.G.

doi:10.1109/icassp.1996.550116

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…In addition, an adaptive filter can be used to remove interference from a signal without affecting crucial information [7], [8]. One key attribute of adaptive filters is their ability to adjust the filtering criteria based on the type of interference to be suppressed [7], [9]. Earlier works that utilized these techniques rely on the use of signal properties such as cyclostationary features [10] inherent in the signal, signal envelope [9], [11], [12] and channel bandwidth [13] to identify and suppress interfering signals.…”

Section: B Suppression Techniquesmentioning

confidence: 99%

“…One key attribute of adaptive filters is their ability to adjust the filtering criteria based on the type of interference to be suppressed [7], [9]. Earlier works that utilized these techniques rely on the use of signal properties such as cyclostationary features [10] inherent in the signal, signal envelope [9], [11], [12] and channel bandwidth [13] to identify and suppress interfering signals. As noted by [14], the major issue with PHY layer techniques that optimize the weights of the filter using estimated timefrequency statistics is that time-varying interference result in time-varying weights which tend to reduce the effectiveness of conventional adaptive filters such as Least Mean Squares (LMS).…”

Section: B Suppression Techniquesmentioning

confidence: 99%

“…architecture is similar to the one described by [126]. The input data could be images as seen in the modified National Institute of Standards and Technology (MNIST) 9 data with size 28 × 28 × 1 [128]. The input image is then passed through the convolution layer that uses convolution filters or kernels to run over the input array and compute a dot product in order to extract different features from the input array.…”

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

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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: B Suppression Techniquesmentioning

confidence: 99%

Section: B Suppression Techniquesmentioning

confidence: 99%

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

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Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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“…In their CNN model (shown in Fig. 6), the first layer consists of a convolution layer and a ReLU activation function while the next four layers consist of batch normalization 9 , convolution and ReLU activation function operations, the last layer (before the output layer) consists of a batch normalization and convolution operations. Convolutional layers in CNNs often learn unique artefacts in the input data that help in identifying interfering signals in the data.…”

Section: A Contributions For Interference Suppression Using Cnnmentioning

confidence: 99%

“…Similar to the TV-FRESH approach, adaptive filters are able to filter or subtract interference from a signal to yield a signal with less interference. One key attribute of adaptive filters is their ability to adjust the filtering criteria based on the type of interference to be suppressed [7], [9]. Earlier works that utilized these techniques rely on the use of signal properties such as cyclostationary features [10] inherent in the signal, signal envelope [9], [11], [12] and channel bandwidth [13] to identify and suppress interfering signals.…”

Section: Introductionmentioning

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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Blind separation of voice modulated single-side-band using the multi-target variable modulus algorithm

Cited by 2 publications

References 7 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

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