The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Jinlong Wang - One of the best experts on this subject based on the ideXlab platform.
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Robust Spectrum Sensing with Crowd Sensors
2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014Co-Authors: Guoru Ding, Fei Song, Qihui Wu, Linyuan Zhang, Shuo Feng, Jinlong WangAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal Data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a Data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal Data parameter configurations.
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Robust Spectrum Sensing With Crowd Sensors
IEEE Transactions on Communications, 2014Co-Authors: Guoru Ding, Qihui Wu, Linyuan Zhang, Jinlong Wang, Yingying ChenAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal Data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing Data with an arbitrary abnormal Component. Under this model, we then analyze the impact of general abnormal Data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing Data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal Component pursuit, and develop a Data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal Data parameter configurations.
Guoru Ding - One of the best experts on this subject based on the ideXlab platform.
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Robust Spectrum Sensing with Crowd Sensors
2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014Co-Authors: Guoru Ding, Fei Song, Qihui Wu, Linyuan Zhang, Shuo Feng, Jinlong WangAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal Data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a Data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal Data parameter configurations.
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Robust Spectrum Sensing With Crowd Sensors
IEEE Transactions on Communications, 2014Co-Authors: Guoru Ding, Qihui Wu, Linyuan Zhang, Jinlong Wang, Yingying ChenAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal Data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing Data with an arbitrary abnormal Component. Under this model, we then analyze the impact of general abnormal Data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing Data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal Component pursuit, and develop a Data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal Data parameter configurations.
Yingying Chen - One of the best experts on this subject based on the ideXlab platform.
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Robust Spectrum Sensing With Crowd Sensors
IEEE Transactions on Communications, 2014Co-Authors: Guoru Ding, Qihui Wu, Linyuan Zhang, Jinlong Wang, Yingying ChenAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal Data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing Data with an arbitrary abnormal Component. Under this model, we then analyze the impact of general abnormal Data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing Data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal Component pursuit, and develop a Data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal Data parameter configurations.
Linyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Robust Spectrum Sensing with Crowd Sensors
2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014Co-Authors: Guoru Ding, Fei Song, Qihui Wu, Linyuan Zhang, Shuo Feng, Jinlong WangAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal Data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a Data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal Data parameter configurations.
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Robust Spectrum Sensing With Crowd Sensors
IEEE Transactions on Communications, 2014Co-Authors: Guoru Ding, Qihui Wu, Linyuan Zhang, Jinlong Wang, Yingying ChenAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal Data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing Data with an arbitrary abnormal Component. Under this model, we then analyze the impact of general abnormal Data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing Data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal Component pursuit, and develop a Data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal Data parameter configurations.
Qihui Wu - One of the best experts on this subject based on the ideXlab platform.
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Robust Spectrum Sensing with Crowd Sensors
2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014Co-Authors: Guoru Ding, Fei Song, Qihui Wu, Linyuan Zhang, Shuo Feng, Jinlong WangAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal Data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a Data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal Data parameter configurations.
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Robust Spectrum Sensing With Crowd Sensors
IEEE Transactions on Communications, 2014Co-Authors: Guoru Ding, Qihui Wu, Linyuan Zhang, Jinlong Wang, Yingying ChenAbstract:This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing Data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal Data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing Data with an arbitrary abnormal Component. Under this model, we then analyze the impact of general abnormal Data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing Data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal Component pursuit, and develop a Data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal Data are jointly exploited to robustly cleanse out the potential nonzero abnormal Data Component from the original corrupted sensing Data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal Data parameter configurations.
