Abstract: The present subject matter relates to a method comprising: determining, for a specific time unit, an available set of frequency resource units, for each signal path of multiple signal paths, determine at least one feature vector descriptive of a set of devices, the set of frequency resource units and the signal path, perform per feature vector of the at least one feature vector an inference pass of a reinforcement machine learning agent model to obtain an individual output using the feature vector as input, and use the at least one individual output to determine an individual scheduling configuration of the set of frequency resource units to the set of devices; using the individual scheduling configurations to determine a scheduling decision to assign the set of frequency resource units to the set of devices, such that a frequency resource unit can be assigned to one or more signal paths.

Abstract: The present subject matter relates to a method for performing for a time unit a scheduling operation comprising: receiving a time domain scheduling decision for the time unit, the time domain scheduling decision indicating a set of devices of the wireless communication system; determining a feature vector descriptive of the set of devices and an available set of frequency resource units of the wireless communication system; inputting the feature vector to a policy-based reinforcement learning agent for receiving an output, the output comprising a distribution between the set of devices and the set of frequency resource units; and using the distribution to determine a frequency domain scheduling decision.

Abstract: The present disclosure relates to a technical solution that improves the reliability of communications over a wireless communication channel by replacing a conventional interleaver (e.g., random interleaver) with a Neural Network (NN)-generated interleaver. For this purpose, a well-trained NN is used, which is configured to receive a UE connection status and a channel status as input data and outputs the interleaver in the form of an orthogonal binary matrix. The NN is shared by a UE and a network node. The network node may use the interleaver to interleave a set of bits in a downlink control message before the downlink control message is encoded, e.g., with an error correcting code, such as a polar code. The UE may generate and transpose the interleaver to obtain a deinterleaver to be applied to the downlink control message after its decoding (e.g., polar decoding).

Abstract: A computer-implemented pre-processed time-delay autoencoder based anomaly detection method are provided for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line.

Abstract: A computer-implemented pre-processed time-delay autoencoder based anomaly detection method are provided for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line.