Jonathan Verlant-Chenet

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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(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(More)
Recent years have shown a growing interest in the concept of Cognitive Radios (CRs), 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.(More)
It has been recently demonstrated that the challenging implementation of the signal detectors can be facilitated by using the compressive sampling theory. In this paper, we consider a network of secondary nodes that cooperate to detect the primary signals by sampling the overall bandwidth periodically at a rate much smaller than the Nyquist rate. The delays(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(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(More)
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