Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

According to one embodiment, a learning device includes a noise generation unit, a decoding unit, a generation unit, and a learning unit. The noise generation unit outputs a second code word which corresponds to a first code word to which noise has been added. The decoding unit decodes the second code word and outputs a third code word. The generation unit generates learning data for learning a weight in message passing decoding in which the weight and a message to be transmitted are multiplied, based on whether or not decoding of the second code word into the third code word has been successful. The learning unit determines a value for the weight in the message passing decoding by using the learning data.

A learning device includes an encoding unit, a plurality of permutation units, a plurality of decoding units, a selection unit, and a learning unit. The encoding unit is configured generate an encoded word by encoding a transmission word. The permutation units are configured to permutate the encoded word according to different permutation manners to generate a plurality of permutated encoded words. The decoding units are configured to perform message passing decoding on the plurality of permutated encoded words, to generate a plurality of decoded words. The message passing decoding involves weighting of values of a word transmitted during the message passing decoding. The selection unit is configured to select one or more of the decoded words. The learning unit is configured to perform learning of weighting values of the weighting based on the transmission word and the selected one or more of the decoded words.

The present disclosure relates to encoding methods and apparatus, and decoding methods and apparatus. In one example encoding method, first input information is obtained. The first input information is encoded based on an encoding neural network to obtain and output first output information. The encoding neural network comprises a first neuron parameter, and the first neuron parameter is used to indicate a mapping relationship between the first input information and the first output information.

A neuromorphic device includes a neuron block, a spike transmission circuit and a spike reception circuit. The neuron block includes a plurality of neurons connected by a plurality of synapses to perform generation and operation of spikes. The spike transmission circuit generates a non-binary transmission signal based on a plurality of transmission spike signals output from the neuron block and transmits the non-binary transmission signal to a transfer channel, where the non-binary transmission signal includes information on transmission spikes included in the plurality of transmission spike signals. The spike reception circuit receives a non-binary reception signal from the transfer channel and generates a plurality of reception spike signals including reception spikes based on the non-binary reception signal to provide the plurality of reception spike signals to the neuron block, where the non-binary reception signal includes information on the reception spikes.

Devices for using a neural network to choose an optimal error correction algorithm are disclosed. An example device includes a decoding controller inputting at least one of the number of primary unsatisfied check nodes (UCNs), the number of UCNs respectively corresponding to at least one iteration, and the number of correction bits respectively corresponding to the at least one iteration to a trained artificial neural network, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network may include a first predicted value indicating a possibility that a first error correction decoding using the first error correction decoding algorithm is successful.

A reliable data transmission method and system based on a predicted amount of data in a vehicle-to-infrastructure (V2I) network is disclosed. A data transmission method of a base station includes determining a maximum amount of data to be transmitted from the base station disposed around a road to a vehicle traveling on the road, determining an encoding number for systematic network coding (SNC) based on the determined amount of data, performing the SNC on original data based on the encoding number and the amount of data, and transmitting encoded data obtained by performing the SNC to the vehicle.

Methods and apparatus for training a Neural Network to recover a codeword of a Forward Error Correction code are provided. Trainable parameters of the Neural Network are optimised to minimise a loss function. The loss function is calculated by representing an estimated value of the message bit output from the Neural Network as a probability of the value of the bit in a predetermined real number domain and multiplying the representation of the estimated value of the message bit by a representation of a target value of the message bit. Training a neural network may be implemented via a loss function.

Methods and apparatus for training a neural network to recover a codeword and for decoding a received signal using a neural network are disclosed. According to examples of the disclosed methods, a syndrome check is introduced at even layers of the neural network during the training, testing and online phases. During training, optimisation of trainable parameters of the neural network is ceased after optimisation at the layer at which the syndrome check is satisfied. Examples of the method for training a neural network may be implemented via a proposed loss function. During testing and online phases, propagation through the neural network is ceased at the layer at which the syndrome check is satisfied.

Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data delayed versions of at least a portion of the respective processing results with coefficient data specific to a processing mode selection. For example, a computing system with processing units may mix the input data delayed versions of respective outputs of various layers of multiplication/accumulation processing units (MAC units) for a transmission in a radio frequency (RF) wireless domain with the coefficient data to generate output data that is representative of the transmission being processed according to a wireless processing mode selection. In another example, such mixing input data with delayed versions of processing results may be to receive and process noisy wireless input data. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.