International audienceBased on previous results on periodic non-uniform sampling (Multi-Coset) and using the well known Non-Uniform Fourier Transform through Bartlett's method for Power Spectral Density estimation, we propose a new smart sampling scheme named the Dynamic Single Branch Non-uniform Sampler. The idea of our scheme is to reduce the average sampling frequency, the number of samples collected, and consequently the power consumption of the Analog to Digital Converter. In addition to that our proposed method detects the location of the bands in order to adapt the sampling rate. In this paper, through we show simulation results that compared to classical uniform sampler or existing multi-coset based samplers, our proposed sampler, in certain conditions, provides superior performance, in terms of sampling rate or energy consumption. It is not constrained by the inexibility of hardware circuitry and is easily recongurable. We also show the eect of the false detection of active bands on the average sampling rate of our new adaptive non-uniform sub-Nyquist sampler scheme
We consider the problem of designing an effective sampling scheme for sparse multi-band signals. Based on previous results on periodic non-uniform sampling (Multi-coset) and recent advances in Compressive Sensing (CS), we propose a new sampling scheme, the Dynamic Single Branch Non-uniform Sampler (DSB-NUS). Our scheme estimates the spectral support using non-uniform spectrum sensing technique to minimize the average sampling rate, thereby reducing the number of samples as well as energy consumption. In this paper, we show through simulation results that compared to existing multi-coset based samplers, our proposed sampler provides superior performance, both in terms of sampling rate and energy consumption. Moreover, we show that our proposed sampler is not constrained by the inflexibility of harware circuitry and is easily reconfigurable.
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