How to Make Analog-to-Information Converters Work in Dynamic Spectrum Environments With Changing Sparsity Conditions

Compressed sensing (CS) analog to information converters (AICs) offer key benefits for signal reception or detection when the input signal is sparse. So far AICs have been demonstrated in environments with controlled input signal conditions and with fixed sparsity levels. This paper investigates how to make AICs effectively operate in dynamic environments with changing signal conditions and thus changing sparsity levels. We focus on RF spectrum scanning, where signals or interferers need to be detected across a wide dynamic RF spectrum, but the presented concepts are widely applicable for low-pass and band-pass CS AICs. The number of measurements and hence the number of branches required in a CS RF front end scales with the sparsity level, i.e. the number of signals that need to be detected. In practice this leads to excessively large silicon area for more than a few signals (e.g., six). We introduce the time-segmented quadrature analog-to-information converter (TS-QAIC), a scalable architecture for signal detection in dynamically changing spectrum environments. While our TS-QAIC prototype implements a fixed number of hardware branches, we experimentally demonstrate adaptive thresholding and adaptive time segmentation to adjust its signal detection capability to the sparsity level of the input signal.