Jonathan Verlant-Chenet | Université libre de Bruxelles (original) (raw)

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Papers by Jonathan Verlant-Chenet

Recent years have shown a growing interest in the concept of cognitive radios (CR), able to acces... more Recent years have shown a growing interest in the concept of cognitive radios (CR), able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low signal-to-noise ratio (SNR) environments, with little or no information about the signals they are trying to detect. Energy detectors are wildly used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible. Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of higher-order cyclostationarity (HOCS), we derive a fourth-order detector that performs similarly to the second-order ones, while being less complex. Performance comparisons between fourth-order, second-order and energy detectors have been performed through simulations to demonstrate the interest of the fourth-order detector.

Cognitive radios are a new technology introduced to resolve the spectrum scarcity problem by supe... more Cognitive radios are a new technology introduced to resolve the spectrum scarcity problem by superimposing new services in the already allocated bands under a non-interference constraint. It has been recently demonstrated that the challenging implementation of the signal detectors can be facilitated by using the theory of compressive sampling. In this paper, we consider a distributed network of secondary nodes that cooperate to detect the primary signals. Each secondary node samples the signal periodically at a rate much smaller than the Nyquist rate. The delays inherent to the propagation channel are used to implement a periodic non-uniform sampling detector when the secondary nodes combine their observations. We demonstrate that the proposed detector can efficiently detect the primary user signal, even under fading channels.

Cognitive radios impose challenges on the design of efficient signal detectors, including wide ba... more Cognitive radios impose challenges on the design of efficient signal detectors, including wide bandwidth sensing and large dynamic range support. The recently considered compressed sensing theory helps in relaxing the constraints on the design of the analog front-end. The maximum likelihood method introduced here is computationally simple since it does not require a signal reconstruction, unlike most methods introduced in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge and the number of occupied bands. The results are supported with Matlab simulations, a statistical study is performed and the probabilities of misdetection and false alarm are plotted for different scenarios, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

We develop a distributed multiband spectrum sensing detector for cognitive radios based on compre... more We develop a distributed multiband spectrum sensing detector for cognitive radios based on compressed measurements that does not rely on signal reconstruction. A fusion centre collects the measurements from different sensing nodes and then makes a sensing decision based on a simplified maximum likelihood criterion which is valid for both analog to information implemented in the paper (MWC and NUP) and does not require prior signal information. Simulation results for probability of erroneous detection and ROC curves show that the performance of the proposed detector is good. Plus, it has a low computational complexity.

Eurasip Journal on Wireless Communications and Networking, 2010

Cognitive radios need devices capable of sensing a large range of frequencies in order to detect ... more Cognitive radios need devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementation of the analog front-ends leads to imperfections. In this paper, we solve this problem with compressed sensing. The introduced maximum likelihood method is computationally simple since it does not require any signal reconstruction, unlike most methods in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge. The results are supported with Matlab simulations, a statistical study is performed and the probability of error is plotted for different cases, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

Cognitive radios are devices capable of sensing a large range of frequencies in order to detect t... more Cognitive radios are devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementations of the analog front-ends leads to imperfections. Two of them are studied in this paper: IQ imbalance and sampling clock offset (SCO). Based on a mathematical system model, we study analytically the impact of the two imperfections on the sensing performance of the energy detector and of the cyclostationarity detector. We show that the IQ imbalance does not impact the performance of the two detectors, and that the SCO only impacts significantly the performance of the cyclostationarity detector.

Recent years have shown a growing interest in the concept of cognitive radios (CR), able to acces... more Recent years have shown a growing interest in the concept of cognitive radios (CR), able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low signal-to-noise ratio (SNR) environments, with little or no information about the signals they are trying to detect. Energy detectors are wildly used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible. Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of higher-order cyclostationarity (HOCS), we derive a fourth-order detector that performs similarly to the second-order ones, while being less complex. Performance comparisons between fourth-order, second-order and energy detectors have been performed through simulations to demonstrate the interest of the fourth-order detector.

Cognitive radios are a new technology introduced to resolve the spectrum scarcity problem by supe... more Cognitive radios are a new technology introduced to resolve the spectrum scarcity problem by superimposing new services in the already allocated bands under a non-interference constraint. It has been recently demonstrated that the challenging implementation of the signal detectors can be facilitated by using the theory of compressive sampling. In this paper, we consider a distributed network of secondary nodes that cooperate to detect the primary signals. Each secondary node samples the signal periodically at a rate much smaller than the Nyquist rate. The delays inherent to the propagation channel are used to implement a periodic non-uniform sampling detector when the secondary nodes combine their observations. We demonstrate that the proposed detector can efficiently detect the primary user signal, even under fading channels.

Cognitive radios impose challenges on the design of efficient signal detectors, including wide ba... more Cognitive radios impose challenges on the design of efficient signal detectors, including wide bandwidth sensing and large dynamic range support. The recently considered compressed sensing theory helps in relaxing the constraints on the design of the analog front-end. The maximum likelihood method introduced here is computationally simple since it does not require a signal reconstruction, unlike most methods introduced in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge and the number of occupied bands. The results are supported with Matlab simulations, a statistical study is performed and the probabilities of misdetection and false alarm are plotted for different scenarios, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

We develop a distributed multiband spectrum sensing detector for cognitive radios based on compre... more We develop a distributed multiband spectrum sensing detector for cognitive radios based on compressed measurements that does not rely on signal reconstruction. A fusion centre collects the measurements from different sensing nodes and then makes a sensing decision based on a simplified maximum likelihood criterion which is valid for both analog to information implemented in the paper (MWC and NUP) and does not require prior signal information. Simulation results for probability of erroneous detection and ROC curves show that the performance of the proposed detector is good. Plus, it has a low computational complexity.

Eurasip Journal on Wireless Communications and Networking, 2010

Cognitive radios need devices capable of sensing a large range of frequencies in order to detect ... more Cognitive radios need devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementation of the analog front-ends leads to imperfections. In this paper, we solve this problem with compressed sensing. The introduced maximum likelihood method is computationally simple since it does not require any signal reconstruction, unlike most methods in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge. The results are supported with Matlab simulations, a statistical study is performed and the probability of error is plotted for different cases, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

Cognitive radios are devices capable of sensing a large range of frequencies in order to detect t... more Cognitive radios are devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementations of the analog front-ends leads to imperfections. Two of them are studied in this paper: IQ imbalance and sampling clock offset (SCO). Based on a mathematical system model, we study analytically the impact of the two imperfections on the sensing performance of the energy detector and of the cyclostationarity detector. We show that the IQ imbalance does not impact the performance of the two detectors, and that the SCO only impacts significantly the performance of the cyclostationarity detector.