Adaptive non-uniform sampling of sparse signals for Green Cognitive Radio

• We have proposed a new smart sampler scheme to reduce the power consumption of the Analog to Digital Converter by reducing its average sampling frequency.
• Its more efficient, in terms of sampling rate, than classical sampler (uniform or multi-coset) when the false detection of bands localisation is low.

Based 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 inflexibility of hardware circuitry and is easily reconfigurable. We also show the effect of the false detection of active bands on the average sampling rate of our new adaptive non-uniform sub-Nyquist sampler scheme.

The false alarm detection effect of our non uniform spectrum sensing block to the average sampling rate on our dynamic single branch non uniform sampler.Figure optionsDownload as PowerPoint slide