58 results about "Convolutional decoding" patented technology

A decoding method for use in a communications system employing a communication channel in which a message is convolutionally encoded by a base station encoder and transmitted to a remote terminal during a time slot allocated to at least that remote terminal. The encoder is not completely reset immediately prior to the allocated time slot such the encoder state is unknown at the onset thereof. The decoding method includes assigning a most likelihood probability for an initial encoder state as being one of a number of predetermined encoder states; and, convolutionally decoding ensuing bits of the message based on the assumption of the initial encoder state as one of the predetermined states. The method affords low error rate decoding, and allows for improved power conservation by a mobile station, since the mobile station need not awaken significantly prior to its allocated time slot. Preferably, a Viterbi decoder is used to perform the convolutional decoding. The method has particular utility when used for decoding page messages indicative of call notifications in a paging channel of a code division multiple access (CDMA) wireless telecommunications system.

Considering both performance and cost of an iterative receiver, the present invention provides an iterative signal receiving method for a wireless communications system. The iterative signal receiving method includes utilizing a channel estimating (CE) process to perform channel estimation for a received signal according to first log-likelihood ratio (LLR) data to generate second LLR data, and then generating the first LLR data according to an error correction code (ECC) decoding process and the second LLR data. When the ECC decoding process is a convolutional decoding process, the CE process is a zero-forcing process, a minimum mean square error (MMSE) process or an interpolation-based process. When the ECC decoding process is a low density parity check code (LDPC) decoding process, the CE process is a maximum likelihood (ML) process or a maximum a posteriori (MAP) process.

The present invention discloses a single unified decoder for performing both convolutional decoding and turbo decoding in the one architecture. The unified decoder can be partitioned dynamically to perform required decoding operations on varying numbers of data streams at different throughput rates. It also supports simultaneous decoding of voice (convolutional decoding) and data (turbo decoding) streams. This invention forms the basis of a decoder that can decode all of the standards for TDMA, IS-95, GSM, GPRS, EDGE, UMTS, and CDMA2000. Processors are stacked together and interconnected so that they can perform separately as separate decoders or in harmony as a single high speed decoder. The unified decoder architecture can support multiple data streams and multiple voice streams simultaneously. Furthermore, the decoder can be dynamically partitioned as required to decode voice streams for different standards.

A constrained turbo block convolutional code (CTBC) involves a serial concatenation of a outer block code B with an inner recursive convolutional code, joined together by a constrained interleaver type 2 (CI-2). The CI-2 interleaver is designed off line, and prior to VLSI design time. The present invention provides massively parallel systems, methods, and apparatus for use in CTBC encoding and decoding. For example, a massively parallel CTBC decoder is be implemented using N processors, each with local private memory, and each with local access to a one or more respective memory locations (e.g., registers) in one or more respective multiported memory banks that each hold extrinsic or related information used in CTBC code iterative SISO decoding. Both the arithmetic decoding operations and the CI-2 interleaving and deinterleaving functions are performed in parallel using the systems, methods, and apparatus of the present invention.

The purpose of the invention is to provide a method for decision of secondary channel equalization of underwater acoustic OFDM. According to the method, a CombMMSE channel estimation is carried out; once equalization is carried out on received data by utilizing a frequency response to obtain post-once equalization data; de-mapping processing is carried out on subcarriers of the post-once equalization data and then estimation of original data is obtained by convolutional decoding or Turbo decoding; coding is again carried out on the decoded data according to an original encoding rule to obtain post-secondary coding data; estimation on block pilot frequency is obtained by taking the estimated data subcarriers as a training sequence and by combining comb pilot frequency subcarriers, so that all subchannels have training data; and then secondary channel estimation is again carried out by utilizing a BlockMMSE algorithm. According to the invention, comb pilot frequency interpolation errors can be substantially reduced; moreover, a good balancing effect can be achieved on the condition that an interval between pilot frequencies is big.

In one embodiment, a file of all the initial states (or their equivalents) and the nth surviving states associated with the initial states is stored along with the path metric. The initial states (or their equivalents) are an index to a previous file. A.new file or files are then generated. An appropriate criterion is utilized to select a final surviving state. The path can be traced back through a plurality of files and the "most likely" path determined. The identifying binary numbers of the final states of each file and the binary numbers of an original initial state determine the "most likely" sequence of convolutionally-encoded symbols received by the decoder. The convolutional decoding can be implemented with a digital signal processor and a dedicated peripheral unit. This apparatus can provide an efficient use of memory for the possible decoding paths.

The invention discloses a parallel multicode-rate convolutional code decoding method and a realization device thereof. The parallel multicode-rate convolutional code decoding method comprises the following steps of: S1. receiving Nin paths of parallel input signals, and shifting the input signals under the effect of a shifting control signal and outputting the input signals, wherein output signals are also Nin paths of parallel signals, and the Nin is positive integers; S2. partially eliminating the phase ambiguity of the output signals; S3. taking the signals processed in the S2 as effectivedata, adding guard intervals for the effective data to assemble into a data frame, and taking the data frame as the input of a parallel convolutional code; S4. carrying out the convolutional decodingon the assembled data frame after being processed in the step S3; and S5. merging multipath outputs of the parallel convolutional decoding into one path and outputting in parallel. The invention provides the parallel multicode-rate convolutional code decoding method aiming at the defect that the throughput rate of the traditional convolutional decoding can not satisfy high-speed communication, improves the decoding throughput rate and the effective payload velocity by adopting a parallel partitioning processing technology, and can satisfy requirements of high-velocity information transmissioncommunication systems, such as satellite communication, and the like.

Techniques for generating soft values for parity bits in a convolutional decoding process are disclosed. An exemplary method comprises, for each of at least one iteration in at least one soft-input soft-output decoder, calculating intermediate probability values for each possible transition between a first plurality of candidate decoder states at a first time and a second plurality of candidate decoder states at a second time. Two or more partial sums are then computed from the intermediate probability values, wherein the partial sums correspond to possible combinations of two or more systematic bits, two or more parity bits, or at least one systematic bit and at least one parity bit. Soft values, such as log-likelihood values, are then estimated for each of at least one systematic bit and at least one parity bit of the received communications data corresponding to the interval between the first and second times, based on the partial sums.

The invention provides a dirty paper coding and decoding method based on a joint lattice forming technology in a cognitive network, and the method is applicable to the occasion that a main network has only one main user and a cognitive wireless network has multiple secondary users. The method includes the specific steps that to guarantee that a signal to noise ratio of a main user receiving end is not changed, secondary user sending ends forward a sending signal of the main user while the secondary user sending ends send the corresponding signals through multiple transmitting antennas of the secondary user sending ends; grouping is conducted on signals transmitted to all secondary user receiving ends through the secondary user sending ends, channel coding and joint lattice forming coding are conduced respectively, and then a coding bit sequence is mapped into a symbol sequence and zero-forcing dirty paper coding is conducted so as to eliminate interference of other users; signals sent out by the main user are overlapped and then transmitted to multiple secondary user receiving ends through a channel by means of multiple transmitting antennas of a main user sending end so as to improve secondary user receiving performance. A traditional channel convolutional decoding method, namely signal restoring, is adopted for the secondary user receiving ends respectively. The method is simple in operation steps, lower in calculation complexity and high in practicability.

The invention discloses a method for suppressing seismic multiples based on a data augmentation training deep neural network, belongs to the technical field of exploration seismic signal processing, and relates to suppression of seismic data multiples and random noise and reconstruction of primary waves. According to the method, a deep neural network with a convolutional coding process and a convolutional decoding process is designed, the convolutional coding process is used for learning and training primary wave features of seismic data in a set, and the convolutional decoding process can utilize the features to reconstruct primary waves and suppress multiples. In the training stage, original data containing multiples and data added with random noise form an augmented data set, and higher anti-noise stability can be achieved by using the data set to learn neural network parameters compared with a neural network trained only by using the original data as input data.

The present invention discloses a butterfly processor capable of performing convolutional decoding and LogMAP decoding in telecommunications systems. First and second add-compare-select modules are provided for receiving input path metrics. A branch metric calculator is also provided for receiving input data and extrinsic data. The branch metric calculator generates output branch metrics to each of the first and second add-compare-select modules. Each of the add-compare-select modules includes a log-sum correction means coupled to compare and select components. A controllable switch selectively couples outputs of the select components and the log-sum corrections means to enable either one of convolutional or LogMAP decoding.

The invention discloses a double-factor authentication method based on double convolutional and cycle encoding, which comprises an encrypting process and a decrypting process. The encrypting process comprises the steps of: carrying out scrambling processing on a palm print feature image of an original palm print image to obtain a first palm print feature secret key; then randomly generating an original secret key, and carrying out convolutional encoding and cycle encoding processing to obtain an intermediate secret key; and carrying out exclusive-or processing on the intermediate secret key and the first palm print feature secret key to obtain encrypted information. The decrypting process comprises the steps of: carrying out exclusive-or processing on the encrypted information and a palm print feature secret key obtained again; and then carrying out convolutional decoding and cycle decoding processing to recover the original secret key. According to the double-factor authentication method disclosed by the invention, double-factor authentication of palm print biological features and a random secret key is implemented, so that security is high; and moreover, in the decrypting process, the original secret key can be completely recovered by carrying out exclusive-or processing on the encrypted information and the scrambled palm print features and then carrying out convolutional decoding and cycle decoding, so that authentication accuracy is improved.

The invention relates to a channel concatenated coding and decoding method for power line carrier communication and an apparatus thereof. According to the coding process, an original information frame sent from an upper computer enters a frame location module for frame head location; after location, the original information frame enters a code scrambling module for code scrambling; after code scrambling, the original information frame enters an RS coding module for RS coding; and after an RS code is output, the original information frame enters a convolutional coding module for convolutional coding, and then the code is output. According to the decoding process, demodulated baseband coding information is sent to a Viterbi decoding module for convolutional decoding; after Viterbi convolutional decoding, original rate information is recovered; a frame synchronization module searches a frame head from the original rate information so as to realize frame synchronization; after frame synchronization, RS decoding data clustering information can be determined; a synchronization result is fed back to the Viterbi decoding module simultaneously; information with the frame synchronized enters an RS decoding module for RS decoding; and finally, a descrambling module descrambles the information so as to recover the original information frame. The channel concatenated coding and decoding method can be applied to a modulation-demodulation device of power line carrier communication, and can improve the anti-interference capacity of signals in transmission, transmission quality and transmission efficiency.

The invention provides a method and a system for correcting errors of decoded data by using a UMTS (Universal Mobile Telecommunications System) receiver. The method comprises the following steps: A, performing CRC (Cyclic Redundancy Check) on convolutional decoded data; B, retrieving in a remainder table. The method and the system have the beneficial effects that the remainder table is constructed by using two significant features of a convolutional decoding error result, and a corresponding error mode is obtained, so that re-correction of the decoded data is realized, and the problem that the conventional UMTS receiver cannot be used for further correcting the data after a convolutional decoding error is solved. The method provided by the invention can be implemented in the form of hardware as well as the form of a software functional module, and is not limited to the combination of hardware and software of any form. Over 50 percent of error packets in convolutional decoding error data packets can be corrected on average, the voice quality and signaling reliability are improved greatly, and the uplink capacity of the system is increased.

The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

The present invention discloses a CNN-based self-supervised voltage sag source recognition method. The method comprises the following steps: collecting voltage sag data, pre-processing the voltage sagdata; constructing a convolutional coder and a convolutional decoder based on an autocoder, to establish a CNN self-supervised model, so that the CNN self-supervised model extracts a feature by usinga convolution layer and a pooling layer, and performs classification by using a BP classification network; dividing the pre-processed voltage sag data into a training set and a test set, inputting the training set into the CNN self-supervised model in batches, to train a feature extraction capability and a classification capability of the CNN self-supervised model; and inputting the test set intothe trained CNN self-supervised model, to perform voltage sag source recognition on the test set. According to the method, a voltage sag source can be recognized accurately.

A method of convolutional decoding with a memory-based Viterbi decoder employs the property of a trace-back path; that is, the similarity between two consecutive trace-back paths becomes higher as the data error rate goes down. Therefore, the method of the invention saves the previous trace-back path into a register, and as soon as the current trace-back path is found to be the same as the previous one, the demanded path is obtained. After that, the memory read operations will stop, thereby reducing the power consumption caused by memory read operations. Prior to path trace-back, the path prediction can be executed by utilizing the property that the minimum path metric and the path are consecutive. The invention reduces the number of memory access operations and power consumption by employing the mechanisms of path matching and path prediction.

Decoding logic is arranged to receive an encoded data signal. The decoding logic comprises a convolutional decoder arranged to perform convolutional decoding on the encoded data signal, to produce a decoded data signal. The decoding logic comprises header bit prediction logic arranged to predict a value for at least one header bit within the decoded data signal, and to provide the predicted value for the at least one header bit to the convolutional decoder to be applied during convolutional decoding.