Abstract: This specification discloses an apparatus and method. The method may comprise receiving a plurality of encoded data messages over a wireless channel from a transmitting node, attempting to decode the received encoded data messages and determining successful/unsuccessful decoding of the encoded data messages. The method may also comprise, responsive to determining successful decoding of a particular encoded data message, allocating the particular decoded data message to a pool associated with the transmitting node, and, responsive to determining unsuccessful decoding of a particular encoded data message, selecting a replacement data message from the pool associated with the transmitting node.

Abstract: Privacy auditing by storing a dataset comprising first data and second data for at least one User Equipment; storing ground truth labels for the first data and the second data; sending information based on the dataset to a network function; receiving, from the network function, information for determining a first value of a loss function; receiving, from the network function, information for determining a second value of the loss function; determining a difference between the first value of the loss function and the second value of the loss function; comparing the difference to a threshold to determine whether a Differential Privacy mechanism was applied to the first data before the network function trained the machine learning model based on the first data.

Abstract: Disclosed is a method comprising providing to a machine learning model, as an input, a channel estimation or a plurality of raw pilots, obtaining, from the machine learning model, as an output, a label indicating a channel model, wherein the channel model is representative of channel conditions, based on the label, determining a waveform associated with the channel conditions, indicating the waveform to the receiver, and performing the transmission to the receiver using the waveform.

Abstract: A method of channel estimation for a receiver side antenna array includes receiving a first signal associated with a pilot tone transmitted by a transmitter side antenna array, obtaining a first group of neural network models trained for channel estimation based on the pilot tone, inputting a representation of the received first signal into each neural network model of the first group, performing one-dimensional interpolation for second signals associated with data tones in at least one of time domain and frequency domain, obtaining a second group of neural network models trained for channel estimation in presence of interpolation errors based on the data tones, and for each one-dimensional interpolation, inputting an interpolated channel estimate of the generated interpolated channel estimates into each neural network model of the second group and generating a corrected interpolated channel estimate for a second signal.

Abstract: An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive a signal to interference noise ratio experienced at a network node based on signal transmission from the apparatus; determine a privacy loss target; select a modulation size, based on the signal to interference noise ratio and the privacy loss target; and transmit, to the network node, at least one symbol using the selected modulation size.

Abstract: A technical solution is provided, which enhances the performance of Machine Learning (ML)-based channel estimation by using two different pilot sequences. More specifically, a first pilot sequence (e.g., a sequence of Demodulation Reference Signals (DMRSs)) is designed to track a change in a wireless communication channel in a frequency domain, while a second pilot sequence (e.g., a sequence of Phase-Tracking RSs (PTRSs)) is designed to track a change in the wireless communication channel in a time domain. By using the first and second pilot sequences thus designed, raw channel estimates in the frequency and time domains, respectively, are obtained on a receiving side. Then, the raw channel estimates are fed to a ML model that is configured to predict a full (time-frequency) channel estimate based thereon. The full channel estimate may be subsequently used in a data decoding algorithm executed on the receiving side.

Abstract: An apparatus, method and computer program is described including: generating a channel quality metric offset; summing a channel quality metric and the channel quality metric offset to generate an adjusted channel quality metric of a channel of a mobile communication system; setting a modulation and coding scheme for transmitting data over the channel based, at least in part, on the adjusted channel quality metric; obtaining feedback data relating to the success of data transfer over said channel; compiling a loss/reward function based, at least in part, on said feedback data; and updating a model using the loss/reward function, wherein the model is used in the generation of said channel quality metric offset.

Abstract: An apparatus, method and computing program is described including: receiving one or more received symbols and one or more received bits, wherein the received symbols are received at a receiver of a transmission system including a transmitter, a channel, and the receiver; converting one or more of the received bits that are deemed to be correct into one or more estimated transmission symbols; generating an estimated channel transfer function based on one or more of the estimated transmission symbols and corresponding received symbols; and providing training data pairs, each training data pair including a first element based on the estimated channel transfer function and a second element based on the corresponding received symbols.

Abstract: It is provided a method, comprising identifying a value of an onsite channel characteristic of a receive channel; requesting a neural network parameter, wherein the request comprises an indication of the onsite channel characteristic; monitoring if the neural network parameter is received in response to the request; estimating the receive channel by a neural network using the neural network parameter if the neural network parameter is received.

Abstract: An apparatus, comprising at least one processor and at least one memory storing instructions, the at least one memory and the instructions configured to, with the at least one processor, cause a receiver to perform a correlation processing of a first signal received by the receiver and/or of a second signal which can be derived from the first signal, and to at least temporarily modify an output signal of the correlation processing using at least one neural network, wherein a modified output signal may be obtained.

Abstract: A method of channel estimation for an antenna array is disclosed. The method includes, receiving a signal transmitted by the antenna array, obtaining a neural network model trained for channel estimation using the received signal, inputting a representation of the received signal into the neural network model and generating a channel estimate for the received signal, and deciding whether to employ a further neural network model for the channel estimation.

Abstract: It is provided a method, comprising identifying a value of an onsite channel characteristic of a receive channel; requesting a neural network parameter, wherein the request comprises an indication of the onsite channel characteristic; monitoring if the neural network parameter is received in response to the request; estimating the receive channel by a neural network using the neural network parameter if the neural network parameter is received.

Abstract: A modulation and coding scheme for a transmission to an apparatus may be selected by at least sampling a posteriori probability distribution that has been calculated using a first probability distribution and a second probability distribution. The first probability distribution is calculated using at least a newest first feedback and a plurality of older first feedbacks, a first feedback indicating channel quality. The second probability distribution is calculated using at least a newest second feedback and a plurality of older second feedbacks, a second feedback indicating a success or failure of an earlier transmission transmitted from the apparatus.

Abstract: A modulation and coding scheme for a transmission to an apparatus may be selected by at least sampling a posteriori probability distribution that has been calculated using a first probability distribution and a second probability distribution. The first probability distribution is calculated using at least a newest first feedback and a plurality of older first feedbacks, a first feedback indicating channel quality. The second probability distribution is calculated using at least a newest second feedback and a plurality of older second feedbacks, a second feedback indicating a success or failure of an earlier transmission transmitted from the apparatus.

Abstract: An apparatus, method and computing program is described including: receiving one or more received symbols and one or more received bits, wherein the received symbols are received at a receiver of a transmission system including a transmitter, a channel, and the receiver; converting one or more of the received bits that are deemed to be correct into one or more estimated transmission symbols; generating an estimated channel transfer function based on one or more of the estimated transmission symbols and corresponding received symbols; and providing training data pairs, each training data pair including a first element based on the estimated channel transfer function and a second element based on the corresponding received symbols.

Abstract: Disclosed is a method comprising obtaining one or more future position estimates of a terminal device, and transmitting the one or more future position estimates to a base station and/or to a location management function, wherein the one or more future position estimates are transmitted in a message comprising a list of one or more pairs of a timestamp and an estimated future position at a time indicated by the timestamp.

Abstract: Disclosed is a method comprising obtaining one or more future position estimates of a terminal device, and transmitting the one or more future position estimates to a base station and/or to a location management function, wherein the one or more future position estimates are transmitted in a message comprising a list of one or more pairs of a timestamp and an estimated future position at a time indicated by the timestamp.