83 results about "Channel decoder" patented technology

The present invention relates to the technical field of cross-modal image signal reconstruction. Disclosed is a cross-modal joint source-channel coding and decoding method adaptable to changeable scenarios. The method comprises: first designing a Transformer encoder-based cross-modal channel coding and decoding optimization solution, so as to achieve the performance improvement and robustness of a channel encoder and a channel decoder; then designing a cross-modal source coding and decoding optimization solution for haptic-to-image generation based on a latent diffusion model, so that under an image signal loss scenario, haptic information is used to guide image generation; and finally, incorporating transfer learning technology, so as to reduce additional training costs caused by a system facing changeable cross-modal communication scenarios such as a changeable channel signal-to-noise ratio and different transmission tasks. Under cross-modal changeable communication scenarios, the joint source-channel coding and decoding method provided in the present invention can solve the problems of the inability of a receiving end to well complete image reconstruction, and additional model training costs caused by changeable channel environments and scenarios.

The invention discloses a semantic communication method for adaptive coding of a semantic source channel under a parallel channel. The method comprises the following steps: S1, constructing a communication model for adaptive coding of the semantic source channel under the parallel channel; s2, at a sending end, encoding semantic information source samples in (X, S), distributing bit streams after encoding each semantic information source sample to an independent channel for transmission, and adopting K channel encoders with different channel rates to protect respective bit streams so as to resist channel errors; s3, at a receiving end, decoding the received signals by using K channel decoders, combining the received signals into a single serial flow, and decoding the single serial flow by using a semantic source decoder based on a deep neural network; s4, determining a source rate and prior information under any given deep neural network model; and S5, constructing an optimization problem, and performing joint optimization of the deep neural network parameter phi and the resource allocation variable # imgabs0 #. According to the invention, resource allocation optimization in a parallel Gaussian channel scene is realized.

The invention discloses a remote speech enhancement transmission method and system based on semantic communication in the technical field of speech signal transmission processing, and the system comprises a local transmitting end which is used for carrying out the short-time Fourier transform of a to-be-enhanced noisy speech signal, and then carrying out the semantic feature extraction of the frequency spectrum of the noisy speech signal through a semantic encoder; carrying out dimension adjustment on the voice semantic features by using a channel encoder; and finally, transmitting to a remote receiving end through a channel. The remote receiving end is used for receiving the voice semantic feature signal transmitted through the channel; after dimension recovery is carried out on a received voice semantic feature signal by utilizing a channel decoder, semantic decoding is carried out by utilizing a semantic decoder to obtain a real part and an imaginary part of a predicted pure voice signal, and finally inverse short-time Fourier transform is carried out to obtain a reconstructed voice signal. According to the invention, under the condition of saving communication resources, the quality of pure voice recovered by a remote receiving end under a low signal-to-noise ratio channel transmission condition can be obviously improved.

The invention relates to a satellite network semantic communication method based on reliable joint source channel coding. The satellite network semantic communication method comprises a signal transmitting method and a signal receiving method. The method comprises the following steps of: 1, extracting features of an image by utilizing a Vision Transform model to obtain semantic representation of the image; 2, constructing a channel estimation and joint loss function; 3, encoding the semantic representation in the step 1 by using a joint source channel encoder and combining the channel estimation in the step 2 and the joint loss function; 4, NOMA power distribution is carried out on the coding result in the step 3, and the coding result is transmitted through a wireless link; the signal receiving method comprises the following steps: step 5, a receiving end receives a signal transmitted in step 4 from a link; and 6, recovering the original user data from the noise by using a joint source channel decoder. According to the method, the semantic information is extracted from the image by using the pre-trained Vision Transform model, so that the amount of data to be transmitted is remarkably reduced, and meanwhile, the communication efficiency is kept.

The invention discloses a simple receiving multiple access method based on joint allocation of symbols and bit streams. The method comprises the following steps: firstly, allocating the symbol streams and the bit streams of all users by a transmitter based on a layered orthogonal amplitude modulation constellation diagram; different interference management schemes are adopted for inter-user interference in different beams and inter-user interference in the same beam, so that the influence of the inter-user interference on the system performance is suppressed; and performing channel equalization processing on signals received by each user receiver, calculating the log-likelihood ratio of each bit stream allocated to each user by each user according to a bit stream allocation result, and sending a calculation result to a channel decoder to obtain original bit data. According to the method, each user symbol and bit stream allocation scheme and constellation diagram design are jointly optimized, and efficient user access in a large-scale access scene can be realized under the condition of meeting the signal processing complexity constraint condition of a receiver.

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.

An apparatus for decoding an encoded multichannel signal includes: a base channel decoder for decoding an encoded base channel to acquire a decoded base channel; a decorrelation filter for filtering at least a portion of the decoded base channel to acquire a filling signal; and a multichannel processor for performing a multichannel processing using a spectral representation of the decoded base channel and a spectral representation of the filling signal, wherein the decorrelation filter is a broad band filter and the multichannel processor is configured to apply a narrow band processing to the spectral representation of the decoded base channel and the spectral representation of the filling signal.

The invention provides a semantic communication system and method and related equipment, and relates to the technical field of communication. The system comprises a transmitter used for calling a corresponding semantic encoder and a channel encoder for data to be transmitted in a target semantic communication scene to perform semantic encoding and channel encoding, and obtaining and sending encoded semantic information; the receiver is used for calling a corresponding channel decoder and a semantic decoder to carry out channel decoding and semantic decoding on the encoded semantic information to obtain recovery data, obtaining the semantic correct rate of the data to be transmitted according to the recovery data and the data to be transmitted, and sending the semantic correct rate to the receiver; and if the semantic correct rate of the to-be-transmitted data is lower than the semantic tolerance of the target semantic communication scene, retransmission indication information is generated and sent to indicate the transmitter to retransmit the to-be-transmitted data. Wherein the semantic tolerance is used for representing the lowest correct rate of the semantic index of the acceptable recovery data in the target semantic communication scene; the numerical value of the semantic index is semantic accuracy.

The invention provides a frequency domain differential modulation method and system for high-reliability and low-delay mobile communication, and a storage medium. The frequency domain differential modulation method comprises the following steps: step 1, converting B-bit information bits into J-bit coded bits; 2, mapping J coding bits into an M-order phase shift keying symbol block, and expressing information as a phase difference of two adjacent subcarrier signals in the same OFDM symbol by using a frequency domain differential modulation technology; step 3, executing K-point inverse discrete Fourier transform to convert the frequency domain transmission sequence into a time domain transmission sequence, and adding a cyclic prefix in the time domain transmission sequence; and 4, receiving the serial sequence by a receiver, calculating K-point discrete Fourier transform of # imgabs0 #, detecting data by comparing subcarrier signals on the same OFDM symbol, and converting the data into B-bit information bits by using a phase shift keying soft demodulator and a channel decoder. The method has the beneficial effects that high-reliability and low-delay mobile communication can be realized with extremely low signaling cost and wireless resources.

Disclosed are methods and systems for using softbit decoding techniques in retransmission-based networks for error concealment of packets corrupted by bit-errors. The softbit decoding techniques derive softbit information from multiple corrupted hardbits of the retransmitted packet to aid a softbit decoder in decoding the packet. The approach realizes improved error concealment capability while maintaining a simple system architecture. A retransmission softbit module is inserted between a channel decoder used for channel-decoding and demodulating a compressed packet and the softbit decoder. The retransmission softbit module may derive an accumulated softbit packet from multiple corrupted copies of the packet received from the channel decoder, make bit decisions based on the accumulated softbit packet, and derive reliability information for the bit decisions. The bit decisions may be a majority decision packet (MDP) created using a majority voting scheme. The reliability information and the MDP may be provided to the softbit decoder for decoding.

The application provides a task-oriented millimeter wave link adaptive multi-modal semantic communication method and device in an automatic driving scene, electronic equipment and a storage medium, and belongs to the technical field of automatic driving. The method comprises the following steps: inputting a target image into a semantic encoder to obtain a first feature map, and then inputting the first feature map into a joint source channel encoder to obtain a second feature map; performing feature screening on the second feature map to obtain a third feature map, and then performing digital precoding and analog precoding on the third feature map to obtain a corresponding sending signal matrix; and then sending the sending signal matrix to a receiving end; performing analog combining and digital combining on the receiving signal matrix to obtain a receiving feature map, and then inputting the receiving feature map into a joint source channel decoder to obtain decoded features; performing fusion processing on the decoded features to obtain fused features, and then inputting the fused features into a semantic decoder to obtain a semantic segmentation result output by the semantic decoder. The application improves the robustness and accuracy of semantic communication.

The present disclosure provides a semantic communication method and device, electronic equipment and storage medium, relates to the technical field of communication, in particular to the technical field of semantic communication. The scheme is: obtaining high-speed mobile user data and low-speed mobile user data; using a first deep joint source channel encoder to encode the high-speed mobile user data to obtain first semantic symbols, and using a second deep joint source channel encoder to encode the low-speed mobile user data to obtain second semantic symbols; performing delay Doppler domain modulation on the first semantic symbols to generate a first signal, and performing time-frequency domain modulation on the second semantic symbols to generate a second signal; superimposing the first signal and the second signal, and sending the superimposed signal to a receiving end; and performing parallel decoding on the receiving end through a first deep joint source channel decoder and a second deep joint source channel decoder to recover the data. The present disclosure suppresses the cliff effect and improves transmission reliability.

Methods, systems, and computer programs encoded on a computer storage medium, that relate to extracting digital watermarks from images, irrespective of distortions introduced into these images. Methods can include inputting a first data item into a channel encoder that can generate a first encoded data item that is greater in length than the first data item and that (1) includes the input data item and (2) new data this is redundant of the input data item. Based on the first encoded data item and a first image, an encoder model can generate a first encoded image into which the first encoded data is embedded as a digital watermark. A decoder model can decode the first encoded data item to generate a second data, which can be decoded by the channel decoder to generate data that is predicted to be the first data.

Methods, systems, and computer programs encoded on a computer storage medium for extracting digital watermarks from images without regard to distortions introduced into those images. The method may include inputting a first data item into a channel encoder that can generate a first encoded data item having a length greater than the first data item and including (1) the input data item and (2) new data that is redundant to the input data item. Based on the first encoded data item and a first image, an encoder model may generate a first encoded image in which the first encoded data is embedded as a digital watermark. A decoder model may decode the first encoded data item to generate a second data, which may be decoded by a channel decoder to generate data predicted to be the first data.

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.

This invention discloses a source-channel joint encoding and decoding method based on a cross-modal communication system, relating to the field of cross-modal image signal reconstruction technology. The method includes: First, designing a cross-modal source encoder, inputting tactile signals and image signals with category labels into the encoder to mine deep-level fusion feature information between modalities. Second, designing a channel encoder and channel decoder, directly mapping the extracted fusion features into a bitstream for channel transmission, improving the system's adaptability to channel changes. Finally, designing a corresponding source decoder at the receiving end to complete high-quality image signal reconstruction from deep-level feature information. The source-channel joint encoding and decoding method provided by this invention reduces power consumption in the network, uses a fitness function to measure the load balance between the cloud network and the server (host), transforming the load balancing problem into an optimization problem, thus making it suitable for resource optimization. Using the particle swarm optimization results as the initial population for the genetic algorithm, this invention achieves better results in terms of execution cost, load balancing, and completion time.

The invention discloses a multi-beam parallax hierarchical modulation multiple access method, which comprises the following steps that: a transmitting end splits bit data after channel coding of each user into a shared part and a private part according to the actual channel condition of each user; the transmitting end performs phase rotation pre-coding on the constellation points and then transmits the constellation points in an overlapped manner, so that the constellation points at the receiving end are aligned in phase The receiving end calculates an equivalent channel coefficient and a receiving end constellation diagram Euclidean distance parameter based on the receiving end channel state information, and performs primary equalization and I / Q path separation processing on a received signal; and a receiving end decodes the bit streams distributed in the shared layer and all the bit streams in the private layer, and after the log-likelihood ratios of all the bit streams are calculated, the log-likelihood ratios are sent to a channel decoder to be recovered to obtain the bit data of each user source. According to the method, a user does not need to perform serial interference cancellation in some working modes, interference of shared part data to private part data can be avoided, and a relatively high user rate is realized.

The invention provides a denoising method and related device for a transformer substation hardware monitoring signal, and the method comprises the steps: obtaining the transformer substation hardware monitoring signal, carrying out the signal preprocessing, and obtaining a preprocessed monitoring signal; inputting the monitoring signal into a pre-trained denoising model for denoising processing so as to obtain a denoised transformer substation hardware monitoring signal; wherein the denoising model extracts local features and global features of a signal from a monitoring signal through a dual-channel encoder; then, the local features and the global features are subjected to denoising processing and then spliced, and fusion features are obtained; performing feature reconstruction processing on the fusion features through a dual-channel decoder to obtain local reconstruction features and global reconstruction features, and splicing the local reconstruction features and the global reconstruction features to obtain final optimization features; and reconstructing the monitoring signal so as to realize de-noising processing. According to the method, the local and global features are extracted respectively, and the noise in the monitoring signal of the transformer substation hardware fitting in the complex noise environment can be removed more thoroughly through denoising, feature reconstruction and other processing.

The invention relates to a depth data-containing image semantic transmission method, which comprises the following steps of: converting an RGBD image into a YCbCr color space, and dividing the YCbCr color space into four channels of brightness y, chromaticity u and v and depth d; in the feature extraction network, semantic feature extraction is performed on each input channel, yf, uf, vf and df are obtained, and yf, uf and vf are merged to obtain yuvf; a joint source channel encoder of a double-branch structure is constructed, one branch receives yuvf, the other branch receives df, and yuvj and dj are output after encoding; transmitting the encoded two paths of features yuvj and dj to a receiving end through a channel; and at a receiving end, inputting the # imgabs0 # and the # imgabs1 # into a joint source channel decoder, performing feature recovery and up-sampling processing to obtain # imgabs2 # and # imgabs3 #, completing detail recovery in a feature recovery network to obtain # imgabs4 # and # imgabs5 #, and finally reconstructing into an RGBD image. According to the method, the data compression capability of the RGBD image can be improved, and the recovery quality of the image is improved under a relatively low signal-to-noise ratio.

The application discloses a kind of semantic communication methods, devices and systems for image classification tasks, the method includes transmitter obtains the semantic feature of given image and is encoded into transmitted image semantic vector based on auto-encoder;Relay node extracts the text feature of given image, and it is multimodal feature fusion with the image feature recovered by image channel decoder, then it is obtained multimodal semantic vector by multimodal channel encoder and is forwarded to receiver;Receiver receives multimodal semantic vector and executes image classification task.The semantic communication method provided by the application introduces the structure of auto-encoder into semantic communication on the one hand, greatly reduces the information redundancy, reduces the occupied communication resources, on the other hand, introduces multimodal semantic relay module, it can supplement complementary text semantic features in relay node, to help receiver better complete image classification task.

To provide a two-pass end-to-end voice recognition system and a computer implementation method without compromising latency and quality.SOLUTION: A method includes: receiving a training example which includes audio data representing a spoken utterance and a ground truth transcription; inserting a placeholder symbol before respective words during utterance; identifying a ground truth alignment for a beginning and an end of the word: and generating a first constrained alignment for a beginning word piece and a second constrained alignment for an ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective words and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective words. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.SELECTED DRAWING: Figure 4

The invention discloses a multi-user source channel adaptive semantic coding method based on deep learning, and the method comprises the following steps: S1, constructing a multi-user semantic communication system based on a deep neural network: at a transmitting end, processing a to-be-transmitted signal through a deep source encoder and a digital channel encoder, and transmitting the to-be-transmitted signal through a digital channel encoder; carrying out superposition numbering and signal transmission; at a receiving end, the received signal is processed by a digital channel decoder and then is processed by a data user decoder and a semantic user decoder to obtain information source data and semantic information; s2, performing end-to-end distortion modeling of the data user and the semantic user based on data regression; and S3, carrying out joint optimization on the source channel coding rate, the power distribution and the beam forming. According to the method, a multi-user broadcast channel is considered, and an information source channel rate, power distribution and beamforming joint optimization algorithm is designed, so that weighting and end-to-end distortion of a multi-user system are reduced to the maximum extent.

The invention discloses a receiving and transmitting device based on negentropy statistics and compressive sensing, and relates to the field of high-speed wireless communication. The device comprises a channel encoder, a modulator, a sparse encoder, a multi-channel transmitter, a multi-channel receiver, a digital preprocessor, an interference eliminator based on negentropy statistics, a signal reconstruction device based on compressive sensing, a symbol synchronizer, a multi-channel demodulator, a channel decoder and the like. According to an interference elimination technology based on negentropy statistics and a nonlinear signal reconstruction technology based on compressive sensing, under the condition that the complexity of the system is not increased, the multi-path high-speed parallel transmission capacity of the system is improved, high-speed reliable transmission is guaranteed, the anti-interference capacity of the system is improved, and the reliability of the system is improved. The antenna is especially suitable for high-speed wireless communication scenes such as microwave communication, mobile communication and scatter communication.

This invention provides a resource allocation method for an image semantic communication system that balances computation and transmission. The image semantic communication system includes a channel encoder, a channel decoder, a semantic decoder, and a semantic encoder with selectable slices. Multiple slices in the encoder have different network depths and widths. The method includes: calculating the computational and transmission loads required by each terminal when selecting different slices; determining the corresponding objectives and constraints based on the communication scenario; modeling an optimization model based on the objectives and constraints; solving the optimization model based on the computational and transmission loads required by each terminal when selecting different slices to obtain a target slice combination and a target resource allocation scheme. The slice combination is the set of slices selected by all terminals, and the resource allocation scheme is the computational and transmission resources allocated to each terminal. This invention, by selecting different slices for encoding and transmission, can flexibly adapt to different communication scenarios and requirements.

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 invention belongs to the technical field of wireless communication and semantic coding, and discloses a radar image decoding method and device based on a communication perception fusion system, and the method comprises the steps: obtaining a target radar image from a radar system through a wireless channel; executing a first decoding operation on the target radar image through a neural network channel decoder module to obtain multiple pieces of image feature information of different sizes and scales of the target radar image; performing a second decoding operation on the plurality of pieces of image feature information with different sizes and scales through a semantic decoder module to obtain semantic feature information of the target radar image; and obtaining a detection result according to the semantic feature information. Therefore, the communication perception fusion system can integrate the radar image recognition process and the communication process of the communication system, so that the utilization rate of computing resources and transmission resources is improved, and the image recognition result and the category label of the radar image can be accurately predicted based on the semantic feature information of the radar image.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments herein disclose methods and systems for parallelizing operations for decoding polar codes and reducing the latency, complexity, and power consumption in the domain of channel coding for beyond 5G networks. Embodiments herein disclose methods and systems for identifying the right number of decoding variables selected from a plurality of pre-defined discrete decoding variables for an Error Correcting Code (ECC) before the decoding process starts, where the method helps in saving significant amount of power.

The present invention relates to a multi-user scheduling method, device and storage medium based on a restricted channel decoder, which is applied to the field of 5G NR communication technology. The method comprises: by judging the number of user data packets, if the number of user data packets exceeds the limited number of users x processed per time slot of the FECA decoder, then caching x user data packets in a first buffer area, caching the remaining user data packets in a second buffer area, first performing CRC processing on the user data packets in the first buffer area in the current air interface time slot, and performing CRC processing on the user data packets in the second buffer area in the time slot next to the current air interface time slot; through the above scheme, the present application can process multiple uplink user data packets at the same time without generating uplink processing conflicts, that is, uplink and downlink processing can be performed in parallel in the current air interface time slot and the next time slot without interfering with each other.

The invention discloses a signal processing method, system and device of a receiver, a medium and a product, and belongs to the technical field of wireless communication technology. The method comprises the following steps: determining a frequency domain receiving signal of a base station, and initializing prior information; performing soft input and soft output equalization processing according to the frequency domain receiving signal, the equivalent frequency domain channel coefficient, the noise power and the prior information to obtain first external information; processing the first extrinsic information by using a soft input soft output channel decoder to obtain a decoding result and second extrinsic information; judging whether the decoding result generated last time is correct or not; if yes, outputting a decoding result generated last time; and if not, updating the prior information according to the second external information generated last time, and entering an iterative updating step. According to the invention, inter-symbol interference and inter-user interference can be suppressed in a multi-user-multiple-input-multiple-output system.

The invention discloses a finite field multiple access system codebook construction method based on EP resource allocation, a minimum branch distance decoding method and a multiple access system, and belongs to the technical field of 6G communication. The problem of low reliability under a block fading channel is solved, and the problem of lack of a decoding method for superposed user information under a finite field in an uplink at present is solved. According to the invention, the codebook is constructed based on UR-EPA, the resources of the whole channel are utilized by allocating a plurality of EPs to a single bit, and the degree of freedom of the whole channel is fully utilized. The invention also designs a decoding method for superposed user information in a finite field, firstly, Turbo iteration is carried out between a basic signal estimator and a channel decoder to realize detection of information bits, and then detection is carried out by utilizing a parity check part based on complex field superposition and patterns, so that joint decoding of check bits is realized.