The Experts below are selected from a list of 3651 Experts worldwide ranked by ideXlab platform
Sofie Pollin - One of the best experts on this subject based on the ideXlab platform.
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unsupervised wireless spectrum anomaly detection with interpretable features
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based anomaly detector for wireless spectrum anomaly detection using power spectral density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alarm rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal Bandwidth, class, and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its anomaly detection capabilities.
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saife unsupervised wireless spectrum anomaly detection with interpretable features
arXiv: Signal Processing, 2018Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal Bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.
Sreeraj Rajendran - One of the best experts on this subject based on the ideXlab platform.
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unsupervised wireless spectrum anomaly detection with interpretable features
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based anomaly detector for wireless spectrum anomaly detection using power spectral density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alarm rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal Bandwidth, class, and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its anomaly detection capabilities.
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saife unsupervised wireless spectrum anomaly detection with interpretable features
arXiv: Signal Processing, 2018Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal Bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.
Wannes Meert - One of the best experts on this subject based on the ideXlab platform.
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unsupervised wireless spectrum anomaly detection with interpretable features
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based anomaly detector for wireless spectrum anomaly detection using power spectral density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alarm rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal Bandwidth, class, and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its anomaly detection capabilities.
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saife unsupervised wireless spectrum anomaly detection with interpretable features
arXiv: Signal Processing, 2018Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal Bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.
Vincent Lenders - One of the best experts on this subject based on the ideXlab platform.
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unsupervised wireless spectrum anomaly detection with interpretable features
IEEE Transactions on Cognitive Communications and Networking, 2019Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based anomaly detector for wireless spectrum anomaly detection using power spectral density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alarm rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal Bandwidth, class, and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its anomaly detection capabilities.
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saife unsupervised wireless spectrum anomaly detection with interpretable features
arXiv: Signal Processing, 2018Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie PollinAbstract:Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a Licensed Band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal Bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.
Thomas Derham - One of the best experts on this subject based on the ideXlab platform.
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Coordinated in-Band ad-hoc transmission underlying cellular networks
2012Co-Authors: Junyi Feng, Letian Rong, Thomas Derham, Samir SaoudiAbstract:In this article ad-hoc communications underlying cellular networks is studied. Two branches of usage cases, which lead to two modes of operation, are analyzed. We propose a coordinated in-Band scheme where the ad-hoc communication uses the same Licensed Band operating the cellular networks and is under the control of cellular network operators. We show that the interference resulting from in-Band ad-hoc transmission can be effectively managed by proper scheduling. The spectral efficiency gain is substantially increased using the proposed scheme.
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VTC Spring - Coordinated In-Band Ad-Hoc Transmission Underlying Cellular Networks
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012Co-Authors: Junyi Feng, Letian Rong, Samir Saoudi, Thomas DerhamAbstract:In this article ad-hoc communications underlying cellular networks is studied. Two branches of usage cases, which lead to two modes of operation, are analyzed. We propose a coordinated in-Band scheme where the ad-hoc communication uses the same Licensed Band operating the cellular networks and is under the control of cellular network operators. We show that the interference resulting from in-Band ad-hoc transmission can be effectively managed by proper scheduling. The spectral efficiency gain is substantially increased using the proposed scheme.