School of Electrical and Computer Engineering Cornell University, Ithaca, NY, United States, CEA, LETI, University Grenoble Alpes, Grenoble, France, School of ECE, Cornell University, Ithaca, NY, United States

Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates, while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of ?1-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications. © 2017 IEEE.

Analog-to-information (A2I) conversion, cognitive radio, compressive sensing, Internet of Things (IoT), radio-frequency (RF) signal acquisition, spectrum sensing, wavelets

Analog to digital conversion, Compressed sensing, Costs, Energy efficiency, Extraction, Feature extraction, Hardware, Internet of things, Radio transceivers, Radio waves, Sensor nodes, Signal processing, Signal reconstruction, Space optics, Analog to informations, Compressive sensing, Internet of Things (IOT), Radiofrequency signals, Spectrum sensing, wavelets, Cognitive radio