Abstract: A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by the at least one hardware processor for: receiving, by a radio frequency (RF) receiver, an RF transmission from an RF transmitter, wherein the RF transmission (a) is modulated in accordance with a specified modulation scheme, and (b) comprises a digital data packet; demodulating the RF transmission to produce a time series of bits that form the digital data packet; and processing the time series of bits to estimate at least one of (i) CFO of the RF transmitter, and (ii) modulation index of the RF transmitter.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.

Abstract: A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor for: receiving, by a radio frequency (RF) receiver, an RF transmission from an RF transmitter, wherein the RF transmission (a) is modulated in accordance with a specified modulation scheme, and (b) comprises a digital data packet; demodulating the RF transmission to produce a time series of bits that form the digital data packet; and processing said time series of bits to estimate at least one of (i) CFO of the RF transmitter, and (ii) modulation index of the RF transmitter.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.