Sub-sampling of channels with time and frequency sparsity access

Abstract

This paper shows that sub-sampling of signals can help in reducing the amount of data to be processed and stored when time and frequency sparsity is considered. The context is the one of the Internet of Things (IoT) for which a huge quantity of users (i.e. objects) communicate with very few time and frequency accesses. Taking advantage of the occupancy theory of probabilities, we propose a theoretical model for such communications and we show that the sampling frequency of signals can be significantly reduced under these assumptions.