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

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: 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: 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: 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 is described including initialising trainable parameters of a receiver of a transmission system, wherein the receiver includes a demodulation module for demodulating received symbols, a quantization module for generating quantized versions of the demodulated symbols and a decoder for generating a decoded output derived from the quantized versions of the demodulated symbols, wherein the demodulation module has at least some trainable weights and the quantization module has at least some trainable weights; receiving a first training sequence of messages at the receiver; obtaining or generating a loss function; and updating at least some of the trainable parameters of receiver based on the loss function, wherein updating at least some of the trainable parameters of receiver includes updating at least some of the trainable weights of the demodulation module and updating at least some of the trainable weights of the quantization module.

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 receiver, a system and a method is described comprising: receiving radio frequency signals at a plurality of receiver modules, wherein each receiver module comprises a plurality of channels, each channel comprising a receiver antenna and a mixer for converting an output of the respective antenna to an analog intermediate frequency signal having a frequency; for each of the plurality of receiver modules, combining the analog intermediate frequency signals of the respective channels into a receiver module analog intermediate frequency signal; and combining the receiver module analog intermediate frequency signals for each of the plurality of receiver modules into a composite output signal, wherein the frequency of the intermediate frequency signal of each channel of each receiver module is different. A similar transmitter, method and transmission system is also described.

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, 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: 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: 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: 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 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.