56 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 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 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 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.

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.

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 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.

The invention discloses a semantic communication system suitable for an OSI network, and relates to the technical field of digital information transmission, comprising: a semantic coding module of a sending end application layer performs semantic extraction on original data based on a semantic extractor to generate a standardized semantic packet containing header information, an AI channel encoder is dynamically called for encoding through a joint encoding module of a physical layer of the sending end according to the head information and the channel state of the standardized semantic packet, and a traditional encoder is called for the self-defined semantic packet to generate a transmission signal; and a receiving end physical layer joint decoding module analyzes the head information of the transmission signal, calls an AI channel decoder to restore the standardized semantic packet after identifying the identifier of the semantic extractor, restores the self-defined semantic packet through a traditional decoder, fuses the standardized semantic packet and the self-defined semantic packet through an application layer semantic decoding module, and reconstructs original data. The method is compatible with a traditional codec, and the cross-device interoperability is ensured by using the unified semantic knowledge base.

The application provides a frequency domain differential modulation method, system and storage medium for high-reliability and low-latency mobile communication, the frequency domain differential modulation method comprising: step 1: converting B-bit information bits into J-bit coded bits; step 2: mapping the J-bit coded bits into an M-order phase shift keying symbol block, and using a frequency domain differential modulation technology to express information as a phase difference between two adjacent subcarrier signals in the same OFDM symbol; step 3: performing K-point inverse discrete Fourier transform to convert a frequency domain transmission sequence into a time domain transmission sequence, and adding a cyclic prefix in the time domain transmission sequence; step 4: a receiver receives a serial sequence, performs K-point discrete Fourier transform, detects data by comparing subcarrier signals on the same OFDM symbol, and converts the data into B-bit information bits by using a phase shift keying soft demodulator and a channel decoder. The application has the beneficial effect that high-reliability and low-latency mobile communication can be realized at extremely low signaling cost and wireless resources.

The invention provides an OTFS system signal detection method based on a Turbo structure, and the method comprises the steps: 1, carrying out the message passing algorithm detection of a channel output signal demodulated by OTFS, and enabling the output mean value and variance to serve as the initial values of MMSE detection in the first iteration; and 2, judging whether the current number of iterations reaches a preset maximum number of times, if so, exiting iteration, and outputting a final detection result. Otherwise, entering the step 3; and step 3, MMSE detection is carried out on the demodulated OTFS signal, a mean value and a variance of the first MMSE detection are provided by a message passing detection algorithm, and a subsequent mean value and a subsequent variance are obtained by operation of extrinsic information output by the LDPC. The MMSE detector calculates extrinsic information of each symbol and sends the extrinsic information to the LDPC channel decoder; 4, the LDPC decoder carries out decoding operation on the information transmitted by the MMSE detector to obtain new extrinsic information; 5, calculating a mean value and a variance of the new extrinsic information, and taking the mean value and the variance as input prior information of the MMSE detector during the next round of iteration; and 6, returning to the step 2, entering a next round of iteration, and adding 1 to the number of iterations.

The invention discloses a text semantic communication system optimization method oriented to multiple tasks. According to the method, firstly, a semantic encoder, a channel encoder, a channel decoder and a task decoder are constructed, the task decoder comprises a plurality of semantic decoders, and each semantic decoder corresponds to a different text communication task. Firstly, a semantic encoder extracts multi-task universal semantic features, after channel encoding, transmission and channel decoding, a denoising module is used for eliminating noise influence, then an MMoE model is introduced into a task decoder, specific task features are extracted through an expert network in the MMoE model, and the task features are extracted through a task decoder. And dynamically calculating weights using the gated network of the model to generate task-related feature representations. And after feature fusion, obtaining a task-specific semantic vector, and finally decoding and outputting the task-specific semantic vector. According to the method, parallel processing of multiple tasks can be realized, and the execution efficiency and accuracy of the multiple tasks are effectively improved.

This invention discloses a speech enhancement method, an electronic device, and a storage medium. The speech enhancement method is used in a speech enhancement system, which includes an encoder module, a dual-channel module, a decoder module, and a merging module. The decoder module includes an excitation decoder, a channel decoder, and a complex spectrum decoder. The method includes: extracting preset features from the original noisy speech signal using the encoder module; modeling the preset features on the frequency axis using the dual-channel module to obtain frequency axis features; modeling the preset features on the time axis using the dual-channel module to obtain time axis features; processing the time axis features using the excitation decoder and channel decoder to obtain a first denoised speech signal; processing the frequency axis features using the complex spectrum decoder to obtain a second denoised speech signal; and merging the first denoised speech signal, the second denoised speech signal, and the original noisy speech signal using the merging module to obtain a final estimated speech signal.

The application discloses a kind of based on source channel joint coding haptic signal reconstruction method, it is related to haptic signal generation technical field, including: the haptic and image signal for training is passed through source encoder, based on classification task preliminary carries out the modal intra-feature extraction and inter-modal feature fusion of haptic and image.Based on the channel encoder of neural network modeling that fusion feature is input, it is used to produce the code stream suitable for channel transmission, after channel transmission, input channel decoder, through the joint training of channel encoder and decoder, the recovered fusion feature is input into haptic decoder, and haptic signal is reconstructed.The haptic signal reconstruction method based on source channel joint coding provided in the application realizes the semantic information fusion of different modal, greatly reduces data volume, improves the robustness of signal to channel change by separately designed channel encoder and channel decoder, and reduces the delay of signal through channel, so that the reconstruction quality of haptic signal is significantly improved.

The invention discloses a cross-modal pedestrian re-identification method based on intermediate modal and multi-attention fusion, and the method comprises the steps: firstly carrying out the modal preprocessing of a visible light image and an infrared image, and inputting the images into a nonlinear modal converter; the converter comprises a bimodal information encoder and a unified channel decoder, and is used for generating an intermediate modal image with modal alignment; then, jointly inputting the generated intermediate modal image and the original visible light and infrared image into a backbone network, and extracting part aggregation features through an intra-modal multi-attention fine-grained feature aggregation module; furthermore, the distribution consistency of the middle modal image is restrained through a loss function of unified modal alignment, the inter-modal alignment effect is optimized, and the cross-modal recognition performance is improved. According to the method, image-level nonlinear mapping and a multi-attention mechanism are utilized, the problems of modal difference and intra-modal heterogeneity between visible light and infrared images are effectively relieved, and the discrimination and robustness of pedestrian identity features are enhanced.

The invention discloses an image semantic communication method and device based on semantic entropy and CSI non-intrusive fusion. The method comprises the following steps: determining estimated CSI based on an original pilot signal and a received pilot signal; when the semantic information entropy of the image semantic information output by the channel decoder is greater than or equal to the dynamic entropy threshold of the channel, performing feature extraction on the estimated CSI to obtain the feature information of the estimated CSI; respectively calculating a value vector, a key vector and a query vector of the semantic information and the feature information of the image, and carrying out vector fusion to obtain enhanced feature representation; performing semantic decoding on the enhanced feature representation to obtain a reconstructed image; according to the method, for a 6G MIMO fading channel, image semantic information and estimated CSI are fused, a more robust semantic feature can be generated, and the feature intrusion problem in a traditional method is avoided.

The application relates to a Gold sequence spread spectrum underwater acoustic modulation and demodulation method against strong multipath interference, relating to underwater acoustic communication. The method comprises the design of a transmitting end modulation and a receiving end demodulation; on the basis of a traditional Gold sequence spread spectrum modulation and demodulation technology, a molecule set method is adopted to divide the traditional GSSS code set into multiple subsets, and different subsets are adopted in the modulation process of each symbol, so that weak correlation is ensured between each symbol. The demodulation process is based on the molecule set modulation method of the modulation process, energy detection is carried out according to the corresponding local GSSS code subset symbol by symbol, the number of bits carried by each symbol is calculated, each bit soft information is calculated, the soft information is input into an interleaver and a channel decoder, and the original information is correctly demodulated. The method overcomes the problem that the traditional GSSS modulation and demodulation technology cannot be correctly demodulated in a strong multipath environment, improves the robustness of the GSSS modulation and demodulation technology to the strong multipath underwater acoustic channel, and realizes stable and robust communication in the strong multipath environment.