6 results about "Channel decoder" patented technology

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

A semantic communication system includes a base station including a semantic encoder configured to convert data to be transmitted into semantic information and a channel encoder configured to convert the converted semantic information into a channel signal and a plurality of user equipment devices (UEs) including a channel decoder configured to receive the channel signal and decode the channel signal into the semantic information and a semantic decoder configured to decode data included in the decoded semantic information based on the decoded semantic information.

A multi-channel decoder circuit associated with a multi-channel decoder system is disclosed. The multi-channel decoder circuit comprises a distributed decoder circuit comprising a set of unit decoder circuits, each unit decoder circuit configured to receive one or more codewords of a plurality of codewords associated with a plurality of input channels, and decode the one or more codewords. The multi-channel decoder circuit further comprises a distribution controller circuit configured to distribute each incoming codeword of the one or more codewords to the respective unit decoder circuit of the set of unit decoder circuits within the distributed decoder circuit, based on determining a currently available unit decoder circuit within the set of unit decoder circuits.

The present disclosure relates to a redundant residue number system (RRNS) channel encoder, a RRNS channel decoder, a RRNS channel encoding method, a RRNS channel decoding method, an electronic device, and a storage medium. The RRNS channel encoder corresponds to a modulus set including k information moduli and n-k redundancy moduli. The RRNS channel encoder includes an input module configured to receive a first k-residue vector including k information residue components; a first to (k-1)-th modulus operation module configured to determine k weights based on the k information residue components and the k information moduli; a k-th modulus operation module configured to determine n-k check residue components based on the k information moduli, the k weights, and the n-k redundancy moduli; and an output module configured to determine a first n-residue vector based on the k information residue components and the n-k check residue components, and send the first n-residue vector to a target channel. The embodiments of the present disclosure can reduce the complexity of digital circuit implementation of the RRNS channel encoder.

There is disclosed a channel decoder apparatus (40) for generating a vectorial feature representation as a vectorial feature representation (40a, 45) in a format specific of a source decoder (50), from a sequence of symbols (36), the vectorial feature representation (40a) representing a source signal (1, 5), the channel decoder apparatus being configured to apply at least one channel-decoder neural network, NN (40a, 42), to the sequence of symbols (36) or another representation (46a, 43a, 42a) of the sequence of symbols, thereby generating the vectorial feature representation in the format specific of the source decoder (50). There is disclosed a channel encoder apparatus (30) for encoding a source signal (1) received as a vectorial feature representation (20a, 21a) from a source encoder (20), the vectorial feature representation (20a, 21a) being in a vectorial format specific of the source encoder (20), the channel encoder apparatus (30) being configured to apply at least one channel-encoder neural network, NN (30a), to the vectorial feature representation (20a, 21a) in the vectorial format specific of the source encoder (20) or to another feature representation (22a, 23a, 24a, 25) of the vectorial feature representation (20a, 21a), to provide a sequence of symbols (35) from the vectorial feature representation (20a, 21a) or the other feature representation (22a, 23a, 24a, 25).

This invention relates to the field of wireless communication channel decoding, specifically a channel decoding method and system based on dynamic channel environments. The method includes: acquiring a received signal; performing synchronization, channel estimation, and demodulation processing on the received signal to obtain an initial log-likelihood ratio vector and a channel-related feature matrix; concatenating and normalizing the initial log-likelihood ratio vector and the channel-related feature matrix to obtain a normalized feature matrix; constructing a log-likelihood ratio correction model; inputting the normalized feature matrix into the log-likelihood ratio correction model to obtain a corrected log-likelihood ratio vector; acquiring a pre-set initial value for a scaling factor; calibrating the corrected log-likelihood ratio vector to obtain a final log-likelihood ratio vector; and inputting the final log-likelihood ratio vector into a channel decoder for decoding. This method solves the problems of lack of adaptability and accuracy in existing channel decoding methods based on dynamic channel environments.