A receiver circuit is disclosed. The receiver circuit includes an amplifier configured to generate an RF signal based on a received signal, where the RF signal includes an information signal and a blocker signal modulating an RF carrier frequency. The receiver circuit also includes an RF filter connected to the amplifier, where the RF filter is configured to selectively attenuate the blocker signal.

A receiver circuit is disclosed. The receiver circuit includes an amplifier configured to generate an RF signal based on a received signal, where the RF signal includes an information signal and a blocker signal modulating an RF carrier frequency. The receiver circuit also includes an RF filter connected to the amplifier, where the RF filter is configured to selectively attenuate the blocker signal.

A device for transmitting data includes a transmitter for generating a frequency-modulated output signal. The transmitter includes a phase-locked loop for adjusting an output frequency of the output signal to a carrier frequency, and a coupling circuit for coupling a data stream into the phase-locked loop. The output signal modulated in frequency by the coupled-in data stream has an output frequency variable over time, and the coupling circuit includes a compensation unit, which couples a compensation signal into the phase-locked loop. The compensation signal compensates at least approximately for an adjustment of the output frequency to the carrier frequency carried out by the phase-locked loop.

The disclosure deals with system and method for an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or edge server (ES). The disclosure adopts the majority vote (MV) principle and defines multiple subcarriers and orthogonal frequency division multiplexing (OFDM) symbols for voting options, which reduces to frequency-shift keying (FSK) over OFDM subcarriers as a special case. Thus, FSK-based over-the-air computation is provided for federated edge learning without channel state information. Since the votes from EDs are separated on orthogonal resources, the proposed scheme eliminates the need for truncated-channel inversion (TCI) at the EDs and allows the ES to detect MV with a non-coherent detector. We also mitigate the peak-to-mean envelope power ratio (PMEPR) of the synthesized signals by using randomization symbols. Simulations show the proposed scheme provides high test accuracy in fading channels for both independent and identically distributed (IID) and non-IID data while resulting in OFDM symbols with lower PMEPRs as compared to one-bit broadband digital aggregation (OBDA) with quadrature amplitude modulation (QAM).

A signal processing device including an acquisition unit configured to acquire signal waveform data corresponding to a frequency signal, a generation unit configured to generate a sine wave and a cosine wave of demodulation waveform data having a demodulation frequency between the first frequency and the second frequency, a first phase calculation unit configured to calculate a first phase based on a multiplication result of the sine wave and the signal waveform data at a first time and a multiplication result of the cosine wave and the signal waveform data at the first time, a second phase calculation unit configured to calculate a second phase based on a multiplication result of the sine wave and the signal waveform data at a second time advanced from the first time by a specified time interval less than one cycle of the demodulation frequency.

A signal processing device including an acquisition unit configured to acquire signal waveform data corresponding to a frequency signal, a generation unit configured to generate a sine wave and a cosine wave of demodulation waveform data having a demodulation frequency between the first frequency and the second frequency, a first phase calculation unit configured to calculate a first phase based on a multiplication result of the sine wave and the signal waveform data at a first time and a multiplication result of the cosine wave and the signal waveform data at the first time, a second phase calculation unit configured to calculate a second phase based on a multiplication result of the sine wave and the signal waveform data at a second time advanced from the first time by a specified time interval less than one cycle of the demodulation frequency.

Embodiments of systems and methods for modulating a downlink signals representative of a communication signal are provided herein. An example method comprises receiving an input signal; in a first one or more processing blocks in a one or more processors, performing a first modulation operation on first data packets of the input signal based on a modulation scheme for a receiver of the downlink signal; in a second one or more processing blocks in the one or more processors in parallel with the first one or more processing blocks, performing a second modulation operation on second data packets of the input signal based on the modulation scheme; and generating a waveform as the downlink signal based on performing the first and second modulation operations.

Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, comparing a classification result for the training vector to the known modulation type to determine modulation classification performance, and modifying a configuration parameter of the consolidated neural network dependent on the determined modulation classification performance.

Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, comparing a classification result for the training vector to the known modulation type to determine modulation classification performance, and modifying a configuration parameter of the consolidated neural network dependent on the determined modulation classification performance.

A signal generation method is used in a transmission device that transmits a plurality of transmission signals from a plurality of antennas at the same frequency and at the same time, in the case where larger power change is performed on a first transmission signal than on a second transmission signal during generation process of the first transmission signal and the second transmission signal, the first transmission signal and the second transmission signal are mapped before the power change such that a minimum Euclidian distance between possible signal points for the first signal is longer than a minimum Euclidian distance between possible signal points for the second signal.