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.

Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks have nonlinear mapping and distributed processing capabilities which are advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein are used to implement error code correction (ECC) decoders.

This application provides a channel encoding method and a communication apparatus. A second communication apparatus obtains a first parameter of a first communication apparatus, where the first parameter includes a parameter related to channel coding and decoding and a reinforcement learning training parameter. The second communication apparatus determines, based on the first parameter, first code construction information for constructing a coded bit sequence based on an information bit sequence during channel encoding; and after sending the first code construction information to the first communication apparatus, performs channel encoding and decoding on communication data between the first communication apparatus and the second communication apparatus by using the first code construction information to improve channel encoding performance and further improve communication reliability.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.

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.

A cyclo-stationary characteristic of a communications channel and/or storage media is determined. The cyclo-stationary characteristic has K-cycles, K > 1. Markov transition probabilities are determined that depend on a discrete phase ϕ=t mod K, wherein t is a discrete time value. An encoder to optimize the Markov transition probabilities for encoding data sent through the communications channel and/or stored on the storage media. The optimized Markov transition probabilities are used to decode the data from the communication channel and/or read from the storage media.

Embodiments herein relate to a method performed by a network node for handling a received signal in a communication network. The network node distributes a first number of inputs of a demodulated signal to a first processing core of at least two processing cores and a second number of inputs of the demodulated signal to a second processing core of the at least two processing cores. The network node further decodes the first number of inputs of the demodulated signal by a first message passing within the first processing core, and decodes the second number of inputs of the demodulated signal by a second message passing within the second processing core. The network node further decodes the demodulated signal by performing a third message passing between the different processing cores over a bus that is performed according to a set schedule.

Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein may be used to implement error code correction (ECC) decoders.

A wireless communication device, including a radiofrequency frontend, configured to wirelessly receive a radiofrequency signal; perform one or more analog baseband operations on the received radiofrequency signal, according to a radio access technology; and output an analog signal representing an output of the analog baseband operations on the received radiofrequency signal; an error corrector, configured to perform an error correction operation on the analog signal; and output an error corrected signal in analog domain; and the analog-digital converter, configured to convert the error corrected signal to digital domain.

A method includes obtaining, for each type of nucleotide, a probability density function, the probability density functions being obtained from measurements of current drops produced during at least one passage of at least one sequence of reference nucleotides through a nanopore sequencer; obtaining measurements of current drops produced when the sequence of nucleotides to be decoded passes through the nanopore sequencer; calculating, for each measurement value considered and for each type of nucleotide of the B types of nucleotides, a piece of reliability information based on the probability density function obtained for the type of nucleotide considered; obtaining a decoded value identifying a type of nucleotide from the B types of DNA nucleotides, by applying a soft decoding algorithm with an error correction code to the current drop measurement and to the B pieces of reliability information obtained for the considered measurement value.