Abstract: According to the present disclosure, a first dataset comprising CSI measurements associated with first UEs and actual positions of the first UEs within a coverage region of a network node is received. Then, a second dataset comprising at least one CSI measurement associated with at least one second UE whose position within the coverage region of the network node is to be estimated is received. After that, based on the first and second datasets, the at least one CSI measurement associated with the at least one second UE is assigned to one of the first UEs that appears closest to the at least one second UE. Finally, the position of the at least one second UE within the coverage region of the network node is estimated by using a semi-supervised machine learning algorithm that receives the first and second datasets and the actual position of the closest first UE.

Abstract: A communications system and method is described comprising: handshaking between a transmitter and a receiver of the communication system to initiate a training procedure, wherein said handshaking comprises a training setup request message comprising parameters for the training procedure, wherein the transmitter comprises trainable parameters and/or the receiver comprises trainable parameters; receiving identified training data from the transmitter at the receiver, wherein the training data comprises transmitter training data and/or receiver training data; sending training information from the receiver to the transmitter, wherein the training information comprises information for controlling training at the transmitter and/or the receiver; and terminating the training procedure.

Abstract: This specification relates to end-to-end learning in communication systems and describes: organising a plurality of transmitter neutral networks and a plurality of receiver neural networks into a plurality of transmitter-receiver neural network pairs, wherein a transmitter-receiver neural network pair is defined for each of a plurality of subcarrier frequency bands of a multi-carrier transmission system; arranging a plurality of symbols of the multi-carrier transmission system into a plurality of transmit blocks; mapping each of said transmit blocks to one of the transmitter-receiver neural network pairs; transmitting each symbol using the mapped transmitter-receiver neural network pair; and training at least some weights of the transmit and receive neural networks using a loss function for each transmitter-receiver neural network pair.

Abstract: To provide demapping at a receiving side, a trained model for a demapper is used to output log-likelihood ratios of received signals representing data in a multi-user transmission. Inputs for the trained model for the demapper comprise a resource grid of equalized received signals.

Abstract: The present subject matter relates to a receiver including a detector for receiving a signal from a transmitter. The detector includes a set of one or more settable parameters, and circuitry configured for implementing an algorithm having trainable parameters. The algorithm is configured to receive as input information indicative of a status of a communication channel between the transmitter and the receiver and to output values of the set of settable parameters of the detector. The detector is configured to receive a signal corresponding to a message sent by the transmitter and to provide an output indicative of the message based on the received signal and the output values of the set of settable parameters of the detector.

Abstract: Neural network performance is improved in terms of training speed and/or accuracy by encoding (mapping) inputs to the neural network into a higher dimensional space via a hash function. The input comprises coordinates used to identify a point within a d-dimensional space (e.g., 3D space). The point is quantized and a set of vertex coordinates corresponding to the point are input to a hash function. For example, for d=3, space may be partitioned into axis-aligned voxels of identical size and vertex coordinates of a voxel containing the point are input to the hash function to produce a set of encoded coordinates. The set of encoded coordinates is used to lookup D-dimensional feature vectors in a table of size T that have been learned. The learned feature vectors are filtered (e.g., linearly interpolated, etc.) based on the coordinates of the point to compute a feature vector corresponding to the point.

Abstract: A method and devices for configuring a data transmission network are disclosed.

Abstract: Apparatuses, systems and methods are described including: converting generator inputs to a generator output vector using a generator, wherein the generator is a model of a channel of a data transmission system and wherein the generator comprises a generator neural network; selectively providing either the generator output vector or an output vector of the channel of the data transmission system to an input of a discriminator, wherein the discriminator comprises a discriminator neural network; using the discriminator to generate a probability indicative of whether the discriminator input is the channel output vector or the generator output vector; and training at least some weights of the discriminator neural network using a first loss function and training at least some weights of the generator neural network using a second loss function in order to improve the accuracy of the model of the channel.

Abstract: An apparatus, method and computer program is described comprising receiving data at a receiver of a transmission system; using a receiver algorithm to convert data received at the receiver into an estimate of the first coded data, the receiver algorithm having one or more trainable parameters; generating an estimate of first data bits by decoding the estimate of the first coded data, said decoding making use of an error correction code of said encoding of the first data bits; generating a refined estimate of the first coded data by encoding the estimate of the first data bits; generating a loss function based on a function of the refined estimate of the first coded data and the estimate of the first coded data; updating the trainable parameters of the receiver algorithm in order to minimise the loss function; and controlling a repetition of updating the trainable parameters by generating, for each repetition, for the same received data, a further refined estimate of the first coded data, a further loss functi

Abstract: A retransmission method in a communication system, wherein the communication system comprises at least one transmitter and at least one receiver with a communication channel between the transmitter and the receiver, the method comprising: utilising in the transmitter a transmitter algorithm with trainable weights and in the receiver a receiver algorithm with trainable weights; generating (302) by the transmitter symbols to be transmitted based on a message to be sent and feedback received from the receiver and transmitting the symbols; generating (304) by the receiver a predicted message based on the received symbols, evaluating (306) the predicted message based on a criterion and providing (312), utilising an algorithm with trainable weights, feedback symbols as a response to the transmitter if the evaluation indicates the predicted message is not acceptable.

Abstract: Disclosed is a method comprising providing a first resource grid as input to a first machine learning algorithm, obtaining a second resource grid as output from the first machine learning algorithm, and transmitting a signal comprising the second resource grid by using orthogonal frequency-division multiplexing modulation.

Abstract: Disclosed is a method comprising providing a first resource grid as input to a first machine learning algorithm, obtaining a second resource grid as output from the first machine learning algorithm, and transmitting a signal comprising the second resource grid by using orthogonal frequency-division multiplexing modulation.

Abstract: An apparatus, method and computer program is described comprising: initialising a plurality of sets of trainable parameters, one set of trainable parameters being initialised for each of a plurality of detection algorithms; obtaining a dataset comprising a plurality of sets of data, each set of data comprising a transmit vector, a receive vector and a channel matrix describing a channel; allocating each of the sets of data to one of a plurality of clusters based on the channel matrix of the respective set of data, wherein each cluster is associated with one of said detection algorithms, wherein the allocation is performed according to a clustering algorithm; and training the trainable parameters of each detection algorithm using the sets of data allocated to the respective cluster.

Abstract: A method comprising receiving a modulated radio signal transmitting coded information bits, performing demodulating on the modulated radio signal, wherein demodulating comprises performing orthogonal time frequency space demodulation, performing equalization on the demodulated radio signal to obtain equalized symbols, obtaining log-likelihood ratios for the coded information bits from the equalized symbols using a trained machine learning model, and reconstructing the coded information bits.

Abstract: In one embodiment, a trainable logic includes determination logic configured to determine a plurality of available receiver configurations and associate each receiver configuration with a context matrix; codebook logic configured to select a quantisation codebook to be used by the trainable logic for the context matrices; and learning logic configured to learn from a training dataset including a plurality of received signal samples relevant to reconstruction of a transmitted message. The learning logic is configured to generate, from the training dataset, a set of superposed parameters and context matrices corresponding to the plurality of available receiver configurations and a set of contextual parameters for each context; quantize the context matrices according to the quantisation codebook; and repeat the generation of superposed parameters, context matrices and quantization of context matrices until a stop criterion is met.

Abstract: An apparatus, method and computer program is described including circuitry configured for using a transmitter algorithm to convert one or more inputs at a transmitter of a transmission system into one or more data symbols, wherein: the transmission system includes the transmitter implementing said transmitter algorithm, a channel and a receiver including a receiver algorithm; the transmitter algorithm includes trainable parameters for converting one or more received data symbols into one or more outputs; and the transmitter algorithm is stochastic.

Abstract: An apparatus, method and computer program is described comprising: initialising trainable parameters of a transmission system having a transmitter, a channel and a receiver; generating training symbols on the basis of a differentiable distribution function; transmitting modulated training symbols to the receiver over the channel in a training mode; generating a loss function based on the generated training symbols and the modulated training symbols as received at the receiver of the transmission system; and generating updated parameters of the transmission system in order to minimise the loss function.

Abstract: A method including determining types of radio access technologies supported by the apparatus, wherein the apparatus supports at least two types of radio access technologies, providing inputs to machine learning models, wherein the machine learning models include a corresponding machine learning model to each type of radio access technology, and for each type of radio access technology parameters associated with that type of radio access technology are provided as an input to the corresponding machine learning model, and obtaining, for each type of the radio access technologies, a power consumption estimate provided by the corresponding machine learning model.

Abstract: Embodiments can include receiving transmissions over a channel at a receiver of a MIMO communication system; the transmissions are received from a plurality of transmitters in communication with the receiver. Each communication is a separate communication having a modulation and coding scheme. An estimate of the channel is converted into an estimate, for each of a plurality of transmitters, of a bit-metric decoding rate for each of a plurality of MIMO detectors. The estimated bit-metric decoding rate for each of the transmitters for each of the MIMO detectors is converted into an estimated bit error rate for each of the MIMO detectors for each of the transmitters. One of the MIMO detectors is selected for use in generating log-likelihood ratios for bits transmitted from the transmitters to the receiver. The one of the MIMO detectors is selected based on the estimated bit-error rates and a target block error rate.

Abstract: An apparatus, computer program and method is described including receiving data at a receiver of a communication system, generating an estimate of the data as transmitted by a transmitter of the transmission system (wherein generating the estimate includes a receiver algorithm having at least some trainable weights), generating a refined estimate of the transmitted data, based on said estimate and an error correction algorithm (wherein, in an operational mode, said estimate of the data as transmitted is generated based on the received data and said refined estimate); and generating, in the operational mode, a revised estimate of the transmitted data on each of a plurality of iterations of said generating an estimate of the transmitted data until a first condition is reached.