Abstract: A method of generating physical channel parameters for wireless channels is described. The method includes: obtaining measurement data based on wireless channel observations; training a generative model with the measurement data such that the generative model learns statistical properties of physical channel parameters based on the measurement data of the wireless channel observations; and generating physical channel parameters by using the trained generative model.

Abstract: The present disclosure relates to a method for generating synthetic wireless channel data, comprising: proving wireless channel data in a latent space, wherein the wireless channel data comprises a plurality of datasets, wherein each dataset represents channel characteristics of a wireless communication channel and comprises a plurality of channel attributes; receiving a user input which defines at least one channel attribute; mutating the wireless channel data, wherein during said mutation only channel attributes of wireless channel data other than the at least one channel attribute defined by the user input are allowed to mutate; and generating synthetic wireless channel data based on the mutated wireless channel data in latent space.

Abstract: This disclosure relates to signal testing, and is concerned with testing a joint communication and sensing (JCAS) signal. A system for testing a JCAS signal received from a device under test (DUT) and a corresponding method are provided. The JCAS signal comprises a communication signal and a sensing signal. The system is configured to perform a signaling test on the communication signal, the signaling test producing a communication response signal, and to perform a sensing test on the sensing signal, the sensing test producing a sensing response signal. Further, the system is configured to send a feedback signal to the DUT, the feedback signal including the communication response signal and the sensing response signal.

Abstract: A measurement device for measuring a resistance of a communication system against an adversarial attack is provided. The measurement device is configured to carry out an adversarial attack on a device under test comprised by the communication system. The measurement device is further configured to measure the resistance of the device under test against the adversarial attack and to carry out the adversarial attack using a radio frequency, RF, signal and generate interference signals by means of a neural network. Also, a device for detection and parameter estimation of an interference source is provided, configured to detect an adversarial attack on a device in a communication system.

Abstract: An electronic device is described. The electronic device includes a signal input, a user interface, a signal processing circuit, and an artificial intelligence circuit. The user interface is configured to detect a first user input, wherein the first user input is associated with an automatic setting of processing parameters of the signal processing circuit. The artificial intelligence circuit is configured to automatically adapt the processing parameters of the signal processing circuit based on the first user input, thereby obtaining a first set of processing parameters. The user interface is configured to detect a second user input, wherein the second user input is associated with an adjustment of the processing parameters of the signal processing circuit. The user interface is configured to adapt the processing parameters of the signal processing circuit based on the second user input, thereby obtaining a second set of processing parameters.

Abstract: This disclosure relates to signal testing, and is concerned with testing a joint communication and sensing (JCAS) signal. A system for testing a JCAS signal received from a device under test (DUT) and a corresponding method are provided. The JCAS signal comprises a communication signal and a sensing signal. The system is configured to perform a signaling test on the communication signal, the signaling test producing a communication response signal, and to perform a sensing test on the sensing signal, the sensing test producing a sensing response signal. Further, the system is configured to send a feedback signal to the DUT, the feedback signal including the communication response signal and the sensing response signal.

Abstract: The present disclosure relates to a system for generating synthetic wireless channel data. The system comprises: an interface unit for receiving measured wireless channel data; a computing device comprising a first AI unit and a second AI unit to be trained; wherein the first AI unit is configured to transfer the measured wireless channel data into latent space data, and wherein the second AI unit is configured to convert the latent space data into synthetic wireless channel data; and wherein the first AI unit and the second AI unit are trained such that the synthetic wireless channel data resembles the measured wireless channel data; and an analyzer unit which is configured to produce correlation data which represents a correlation between the measured and/or the synthetic wireless channel data and at least one attribute of the latent space data.

Abstract: The present disclosure relates to a method for generating synthetic wireless channel data, comprising: proving wireless channel data in a latent space, wherein the wireless channel data comprises a plurality of datasets, wherein each dataset represents channel characteristics of a wireless communication channel and comprises a plurality of channel attributes; receiving a user input which defines at least one channel attribute; mutating the wireless channel data, wherein during said mutation only channel attributes of wireless channel data other than the at least one channel attribute defined by the user input are allowed to mutate; and generating synthetic wireless channel data based on the mutated wireless channel data in latent space.

Abstract: The invention relates to a system for generating synthetic digital data, comprising: a receiver configured to receive at least one measured signal, in particular an RF signal or a sensor signal, a converter configured to convert the at least one measured signal to a digital dataset representing signal characteristics of the at least one measured signal, at least one trainable neural network encoder, wherein, during a training routine, the neural network encoder is configured to receive the digital dataset and to generate a compressed representation of the digital dataset, a processing unit configured to analyze the compressed representation and to detect a correlation between the digital dataset and the compressed representation, wherein the processing unit is configured to generate decoder input data based on the detected correlation, and a trained neural network decoder which is configured to receive the decoder input data and to generate synthetic digital data representing signal characteristics of the at

Abstract: The invention relates to a system for generating synthetic digital data, comprising a receiver configured to receive a measured signal, in particular an RF signal, a converter configured to convert the measured signal to digital data representing signal characteristics of the measured signal, a trainable neural network encoder and a trainable neural network decoder, wherein, during a training routine, the neural network encoder is configured to receive the digital data and to generate a compressed representation of the digital data, and the neural network decoder is configured to generate a reconstruction of the digital data based on the compressed representation, and wherein the trained neural network decoder is configured to receive random or pseudorandom data and to generate synthetic digital data representing measured signal characteristics based on the random or pseudorandom data.

Abstract: An electronic device is described. The electronic device includes a signal input, a user interface, a signal processing circuit, and an artificial intelligence circuit. The user interface is configured to detect a first user input, wherein the first user input is associated with an automatic setting of processing parameters of the signal processing circuit. The artificial intelligence circuit is configured to automatically adapt the processing parameters of the signal processing circuit based on the first user input, thereby obtaining a first set of processing parameters. The user interface is configured to detect a second user input, wherein the second user input is associated with an adjustment of the processing parameters of the signal processing circuit. The user interface is configured to adapt the processing parameters of the signal processing circuit based on the second user input, thereby obtaining a second set of processing parameters.

Abstract: The invention relates to a system for generating synthetic digital data, comprising: a receiver configured to receive at least one measured signal, in particular an RF signal or a sensor signal, a converter configured to convert the at least one measured signal to a digital dataset representing signal characteristics of the at least one measured signal, at least one trainable neural network encoder, wherein, during a training routine, the neural network encoder is configured to receive the digital dataset and to generate a compressed representation of the digital dataset, a processing unit configured to analyze the compressed representation and to detect a correlation between the digital dataset and the compressed representation, wherein the processing unit is configured to generate decoder input data based on the detected correlation, and a trained neural network decoder which is configured to receive the decoder input data and to generate synthetic digital data representing signal characteristics of the at

Abstract: The invention relates to a system for generating synthetic digital data, comprising a receiver configured to receive a measured signal, in particular an RF signal, a converter configured to convert the measured signal to digital data representing signal characteristics of the measured signal, a trainable neural network encoder and a trainable neural network decoder, wherein, during a training routine, the neural network encoder is configured to receive the digital data and to generate a compressed representation of the digital data, and the neural network decoder is configured to generate a reconstruction of the digital data based on the compressed representation, and wherein the trained neural network decoder is configured to receive random or pseudorandom data and to generate synthetic digital data representing measured signal characteristics based on the random or pseudorandom data.