Abstract: An apparatus, method and computer program is described comprising: transmitting signals from a transmitter of a multiple-input single-output transmission system to a receiver of the transmission system, wherein the transmitter communicates with the receiver over a plurality of channels of the transmission system, wherein the transmitter includes a transmitter algorithm having at least some trainable weights, wherein said transmitter algorithm converts a sequence of coded bits into time domain baseband symbols for transmission over said channels; updating weights of said transmitter algorithm based on a loss function, said loss function having a first loss term, a second loss term and a variable defining a weighting of those loss terms, wherein the first parameter relates to an information rate of communications from the transmitter to the receiver; and repeating the transmitting and updating until a first condition is reached.

Abstract: Various embodiments and implementations of graph-neural-network (GNN)-based decoding applications are disclosed. The GNN-based decoding schemes are broadly applicable to different coding schemes, and capable of operating on both binary and non-binary codewords, in different implementations. Advantageously, the inventive GNN-based decoding is scalable, even with arbitrary block lengths, and not subject to typical limits with respect to dimensionality. Decoding performance of the inventive GNN-based techniques demonstrably matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while exhibiting improvements with respect to number of iterations required and scalability of the GNN-based approach. These inventive concepts are implemented, according to various embodiments, as methods, systems, and computer program products.

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: To obtain delay spread estimations and/or Doppler spread estimations, data representing received data is input to at least one trained model, the trained model outputting spread estimations.

Abstract: A central node receives, from each of a plurality of access points providing access for user equipments to a communication network, a result of a measurement related to a position of a user equipment, receives additional information comprising at least one of an evaluation of the result of the measurement and a reference towards which the measurement was done, and calculates the position of the user equipment by combining the results of the measurements received from the plurality of access points using at least one of the evaluation and the reference.

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: Different solutions for an apparatus comprising a predictor predicting decodability of received symbols are disclosed. Decoding is performed based on the prediction.

Abstract: A method, apparatus and computer program is described, comprising: obtaining a first beam alignment dataset, wherein the first beam alignment dataset comprises measurement data for a first plurality of beam pair transmissions, wherein each beam pair transmission of the first plurality is between one of a plurality of communication beams of a first user device and one of a plurality of communication beams of a base station and wherein the first plurality of beam pair transmissions is a subset of all available beam pair transmissions between the first user device and the base station; and selecting a first beam pair combination for communications between the first user device and the base station, wherein the first beam pair combination comprises one of the plurality of beams of the first user device and one of the plurality of beams of the base station, wherein the means for selecting the first beam pair combination comprises a machine-learning model trained with a second beam alignment dataset.

Abstract: An apparatus for optimization of signal shaping for a multi user multiple input multiple output, MU-MIMO, communication system, including circuitry configured for receiving a bit vector; and for determining a constellation vector, wherein the circuitry for determining the constellation vector includes a Geometric Shaping and Labeling Block, GSLB, for modulating the bit vector, wherein the GSLB is configured to implement an algorithm with one or more trainable parameters.

Abstract: Various embodiments and implementations of graph-neural-network (GNN)-based decoding applications are disclosed. The GNN-based decoding schemes are broadly applicable to different coding schemes, and capable of operating on both binary and non-binary codewords, in different implementations. Advantageously, the inventive GNN-based decoding is scalable, even with arbitrary block lengths, and not subject to typical limits with respect to dimensionality. Decoding performance of the inventive GNN-based techniques demonstrably matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while exhibiting improvements with respect to number of iterations required and scalability of the GNN-based approach. These inventive concepts are implemented, according to various embodiments, as methods, systems, and computer program products.

Abstract: Neural network-based structures for action user equipment device detection, estimation of time-of-arrival, and estimation of carrier frequency offset utilized with the narrowband physical random-access channel of wireless communication systems. The structure includes a neural network to generate predictions of active user equipment devices, and a twin neural network to generate time-of-arrival predictions for signals from the user equipment devices and carrier frequency offset predictions for signals from the user equipment devices.

Abstract: An apparatus, method and computer program performs initializing parameters of a transmission system. The transmission system comprises a transmitter, a first channel, a relay, a second channel and a receiver. The transmitter includes a transmitter algorithm having trainable weights, the relay includes a relay algorithm having trainable weights and the receiver includes a receiver algorithm having trainable weights. A first training sequence of messages is received, and the first training sequence of messages is sent from the transmitter to the relay using the first channel and is sent from the relay to the receiver using the second channel. A loss function is obtained or generated, and trainable parameters of the transmission system are updated based on the loss function. The trainable parameters include some of the trainable weights of the transmitter, some of the trainable weights of the relay, and some of the trainable weights of the receiver.

Abstract: A communication system including at least one receiver and at least one transmitter is described. The receiver is configured to communicate with the transmitter using a plurality of resource elements within a time-frequency OFDM grid. The transmitter is configured to transmit data-carrying symbols using all resource elements of the plurality of resource elements within the OFDM grid, wherein the data-carrying symbols are modulated using a constellation C. The receiver is configured to receive the data-carrying symbols on all resource elements of the plurality of resource elements within the OFDM grid, and to implement a neural network, configured to operate jointly on the plurality of resource elements, and to output, based on the received data-carrying symbols, a plurality of LLRs to reconstruct information bits from the received data-carrying symbols. The neural network is optimized with respect to the constellation C.

Abstract: A method and an apparatus for identifying transmitting radio devices from a plurality of radio deviceswithin a video feed is described. At least one radio signal identifying a transmitting radio device is received, from the plurality of radio devices. A video feed identifying radio deviceswithin a field of view of the computer vision (CV)system is received, from the CV system. A first set of features is extracted from the received at least one radio signal and a second set of features is extracted from the received video feed. The first set of features and the second set of features are provided as an input to a machine learning (ML) algorithm to obtain a relationship between the transmitting radio device and the radio devicesidentified in the video feed.

Abstract: Disclosed is a method for determining open loop power control parameters. A first set of key performance indicators associated with a first set of open loop power control parameters is obtained from one or more first base stations. A statistical model is determined based at least partly on the first set of key performance indicators and the first set of open loop power control parameters. A second set of open loop power control parameters is determined based at least partly on the statistical model.

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: An apparatus, method and computer program is described comprising: generating a first loss function component comprising comparing first location data with first location estimates, wherein the first location estimates are based on channel state data, wherein the first location estimates are generated using a model, and wherein the model comprises a plurality of trainable parameters; generating a second loss function component comprising comparing the first location data with second location estimates, wherein the second location estimates are based on channel state data that have been subjected to a first augmentation and wherein the second location estimates are generated using the model; generating a third loss function component comprising comparing third location estimates based on channel state data and fourth location estimates based on channel state data that have been subjected to a second augmentation, wherein the third and fourth location estimates are generated using the model; and training the tr

Abstract: This specification describes an apparatus relating to rate adaptation. The apparatus may comprise a processor and a memory including instructions, the instructions, when executed by the processor, cause the apparatus to provide first data representing an estimate of a communications link based on a signal received over said communications link from a transmitter. The apparatus may determine an estimated achievable data rate over said communications link for each of a plurality of link configurations which have respective combinations of modulation scheme and pilot symbol pattern which correspond to one or more transmitter link configurations, the estimated achievable data rate for a particular link configuration being determined based on the first data, and the modulation scheme and the pilot pattern of the particular reference link configuration. The apparatus may select a transmitter link configuration based on the estimated achievable data rates.

Abstract: An apparatus, method and computer program is described including: receiving, at a receiver of a transmissions system, transmitted signals from each of a plurality of transmitters, wherein each transmitter communicates with the receiver over one of a plurality of channels of the transmission system, wherein each transmitter includes a transmitter algorithm having at least some trainable weights, wherein each transmitter algorithm has the same trainable weights and wherein each of the transmitted signals is based on a perturbed channel symbol generated at the respective transmitter, wherein the channel symbols and perturbations are known to the receiver; updating the weights of the transmitter algorithm, at the receiver, based on a loss function; providing the updated weights to each transmitter of the transmission system; and repeating the receiving and updating until a first condition is reached.

Abstract: An apparatus, method and computer program product is disclosed. The apparatus may comprise means for receiving a performance metric for an antenna array comprised of a plurality of radiating elements, the performance metric being based on performance data associated with the antenna array, the antenna array having a radiating configuration represented by configuration parameters. The apparatus may also comprise means for updating the configuration parameters dependent on the received performance metric by means of estimating new configuration parameters for moving the performance metric towards a target value. The apparatus may also comprise means for re-configuring the radiating configuration of the antenna array based on the updated configuration parameters such that the physical geometry of the antenna array is changed.