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: 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: 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: 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: 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: A network-based apparatus includes at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the network-based apparatus to: select at least a first bitstream from a central repository based on an indicator associated with a probability of concurrent, simultaneous or future execution of the first bitstream and a second bitstream at a network node, each of the first bitstream and the second bitstream including programming information for a device at the network node, the indicator being based on an embedding matrix mapping at least a subset of bitstreams in the central repository to an N-dimensional vector of real numbers; and output the first bitstream to the network node for storage and execution upon request.

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 evaluating some or all of a current population of algorithms according to a metric, each algorithm of the population implementing a transmission system; selecting a subset of the algorithms of the current population based on the metric; generating an updated population of algorithms from said subset; and repeating the evaluating, selecting and generating, based on the updated population, until a first condition is reached.

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: An apparatus, method and computer program is described comprising: initialising parameters of a transmission system, wherein the transmission system comprises a transmitter, a channel and a receiver, wherein the transmitter includes a transmitter algorithm having at least some trainable weights and the receiver includes a receiver algorithm having at least some trainable weights; updating trainable parameters of the transmission system based on a loss function, wherein the trainable parameters include the trainable weights of the transmitter and the trainable weights of the receiver and wherein the loss function includes a penalty term; quantizing said trainable parameters, such that said weights can only take values within a codebook having a finite number of entries that is a subset of the possible values available during updating; and repeating the updating and quantizing until a first condition is reached.

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: 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.

Abstract: According to an aspect, there is provided an apparatus comprising a combiner for combining a received analog signal with an analog dithering signal to produce a combined analog signal, a one-bit analog-to-digital converter for converting the combined analog signal to a combined digital signal, means for performing joint downsampling and feature extraction for the combined digital signal, means for implementing a trained machine-learning algorithm for calculating one or more input parameters for waveform generation at least based on one or more features extracted from the combined digital signal and a parametric waveform generator for generating the analog dithering signal based on the one or more input parameters.

Abstract: A method, apparatus and computer program are described includes obtaining or generating a transmitter-training sequence of messages for a first transmitter of a first module of a transmission system, wherein the transmission system includes the first module having the first transmitter and a first receiver, a second module having a second transmitter and a second receiver, and a channel, wherein the first transmitter includes a transmitter algorithm having at least some trainable weights; transmitting a perturbed version of the transmitter-training sequence of messages from the first transmitter to the second receiver over the channel of the transmission system; receiving a first loss function at the first receiver from the second transmitter, wherein the first loss function is based on the transmitted perturbed versions of the transmitter-training sequence of messages as received at the second receiver and knowledge of the transmitter-training sequence of messages for the first transmitter of the transmissio

Abstract: A method, apparatus, receiver and system provide for configurability of the modulation order of a communications system, such as an autoencoder-based communication system. With respect to a system including a transmitter and a receiver, the transmitter encodes a message into a vector of channel symbols for transmission via a channel. The message is encoded pursuant to a modulation order m that is adjustable up to a maximum modulation order Mmax. The receiver receives a vector of samples generated by the channel and determines a first prediction vector for the message encoded by the transmitter. The receiver includes a slicing layer to eliminate one or more elements of the first prediction vector if the modulation order m is less than the maximum modulation order Mmax so as to generate a second prediction vector tailored to the modulation order from which a prediction of the message encoded by the transmitter is identified.

Abstract: To support a wide range of code rates in probabilistic shaping based modulation schemes solutions, a constellation which is at least based on a trained model is used in the modulation. Depending on an implementation the trained model may be a constellation comprising a plurality of sub-constellations with trained parameters as constellation points, or the trained model may be for generating a constellation and corresponding constellation points.