A configurable acquisition engine for direct sequence (DS) spread spectrum (SS) is provided that is reconfigurable without increasing memory size for several use cases having different time-frequency uncertainties. The acquisition engine utilizes a frequency-domain decimation filter to reduce the number of output frequency points while still utilizing information from all frequency bins.

The present disclosure provides a method and a system to estimate LO leakage and quadrature error parameters for a transmitter RF front end, such as a direct up-conversion transmitter RF front end, in a joint fashion. The proposed method utilizes a PN sequence inserted at the transmitter baseband. At the observation receiver side, an RX accumulator is implemented to sum receiver signals to take advantage of a despreading gain using the same PN sequence from transmitter side. Through the despreading process, the receiver-transmitter channel may be estimated and used to extract the quadrature error parameters. The estimated channel may also be used to eliminate user data interference presented within the RX accumulator output, which may further be used to compute the LO leakage.

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.

A wireless communication device comprises a first communication unit, a second communication unit and a single control unit. The first communication unit wirelessly communicates by a first communication signal according to a first communication standard. The second communication unit wirelessly communicates by a second communication signal according to a second communication standard. The second communication signal has a frequency band that overlaps with that of the first communication signal. The second communication standard is different from the first communication standard. The control unit generates a first interference suppression signal for suppressing interference in the second communication signal and a second interference suppression signal for suppressing interference in the first communication signal, and suppresses the interference in the first communication signal and the interference in the second communication signal based on the first interference suppression signal and the second interference suppression signal.

An interference removal filter that includes a combination of a first filter and a second filter, where the first filter passes signals over a frequency range of size B with a variation of less than +/−3 dB, where the peak value of the impulse response of the second filter is displaced in time from the peak value of the impulse response of the first filter by at least 2/B time units, and where the combination of the first filter and the second filter produces a notch in frequency at a frequency location within the frequency range.

A controller is configured to control the adaptive notch filter and to execute a search technique (e.g., artificial intelligence (AI) search technique) to converge on filter coefficients and to recursively adjust the filter coefficients of the adaptive notch filter in real time to adaptively adjust one or more filter characteristics (e.g., maximum notch depth or attenuation, bandwidth of notch, or general magnitude versus frequency response of notch).

A controller is configured to control the adaptive notch filter and to execute a search technique (e.g., artificial intelligence (AI) search technique) to converge on filter coefficients and to recursively adjust the filter coefficients of the adaptive notch filter in real time to adaptively adjust one or more filter characteristics (e.g., maximum notch depth or attenuation, bandwidth of notch, or general magnitude versus frequency response of notch).

A controller is configured to control the adaptive notch filter and to execute a search technique (e.g., artificial intelligence (AI) search technique) to converge on filter coefficients and to recursively adjust the filter coefficients of the adaptive notch filter in real time to adaptively adjust one or more filter characteristics (e.g., maximum notch depth or attenuation, bandwidth of notch, or general magnitude versus frequency response of notch).

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.

A controller is configured to control the adaptive notch filter and to execute a search technique (e.g., artificial intelligence (AI) search technique) to converge on filter coefficients and to recursively adjust the filter coefficients of the adaptive notch filter in real time to adaptively adjust one or more filter characteristics (e.g., maximum notch depth or attenuation, bandwidth of notch, or general magnitude versus frequency response of notch).