Abstract: Exemplary embodiments of the present disclosure are directed to a machine learning-based receiver that operates on the basis of a wide-band RF front-end. The RF front-end outputs clean IQ data to a SPectrum-Analysis-Isolation-Synthesis (SPAIS) system in order to collect analytics of signals present within the monitored band of frequencies, including signal center frequencies, bandwidth and power. The SPAIS system pre-processes the captured wideband IQ data in order to locate frequency bands with signal energy, isolate and synthesize the located frequency bands in corresponding IQ data for individual processing by a neural network with high classification accuracy.

Abstract: A radio frequency (RF) transceiver includes a reference signal source to generate a reference signal, a local RF source to generate a local RF signal and a mixed-signal phase/frequency detector to compare the reference signal to the local RF signal, and to generate a difference signal from the comparison, wherein the difference signal comprises a modulation component and an error component. The transceiver also includes a receiver front end to receive and downconverts an angle-modulated RF signal to a baseband signal, a quadrature modulator configured to angle-modulate the reference signal source with the baseband signal.

Abstract: A radio frequency (RF) transceiver includes a reference signal source to generate a reference signal, a local RF source to generate a local RF signal and a mixed-signal phase/frequency detector to compare the reference signal to the local RF signal, and to generate a difference signal from the comparison, wherein the difference signal comprises a modulation component and an error component. The transceiver also includes a receiver front end to receive and downconvert an angle-modulated RF signal to a baseband signal, a quadrature modulator configured to angle-modulate the reference signal source with the baseband signal.