Assessment of analog-to-information converters based on non-uniform sampling for spectrum analysis

Nowadays, the development of a new open architecture for Analog-to-Information Conversion (AIC) represents an open field of research. This paper presents the preliminary exploration of two AIC architectures concerning Non-Uniform Sampling (NUS), and a derivation of it, called Non-Uniform Wavelet Band-pass Sampling (NUWBS). To this goal, based of the existing mathematical models of these AIC architectures, a numerical investigation has been carried out in order to assess their capabilities in terms of Compressed Sensing (CS) framework for spectrum analysis. By considering non-idealities such as random jitter and noise affecting the AIC architectures, an evaluation of two Figures-of-Merit (FoM) has been accomplished. The obtained results highlight that from a hardware implementation point of view of both AIC architectures, the NUWBS is not restricted to use control signals for the NUS process at the Nyquist rates, as in case of NUS AIC architecture. Moreover, the reported results indicate that the NUWBS AIC architecture is advantageous for the future Wideband Spectrum Sensing (WSS) applications, such as those required in case of Cognitive Radios (CR) for 5G telecommunications and for Internet-of-Things (IoT) devices.