50 results about "Sum product algorithm" patented technology

Systems and methods for generating check node updates in the decoding of low-density parity-check (LDPC) codes use new approximations in order to reduce the complexity of implementing a LDPC decoder, while maintaining accuracy. The new approximations approximate the standard sum-product algorithm (SPA), and can reduce the approximation error of min-sum algorithm (MSA) and have almost the same performance as sum-product algorithm (SPA) under both floating precision operation and fixed-point operation.

The present invention relates to a decoding method and a decoder, a program, a recording-and-reproducing apparatus and a method, and a reproducing apparatus and a method that are suitable for decoding encoded data encoded by using a linear code on ring R. A low-density processing unit performs parity-check-matrix low-density processing, performs linear combination for rows of a parity check matrix included in an obtained reception word, and generates a parity check matrix according to the linear-combination result, thereby reducing the density of the parity check matrix used for decoding, at step S21. Then, at step S22, an LDPC decoding unit performs decoding by using a sum product algorithm (SPA) by using the parity check matrix whose density is reduced through the processing performed at step S21. Where the processing at step S22 is finished, the LDPC decoding unit finishes decoding for the reception word. The present invention can be used for an error-correction system.

The check matrix generation method for LDPC codes of the present invention analyzes the "Sum-Product Algorithm" of LDPC codes by assuming that the log-likelihood ratio of the input and output data in the decoder can be approximated to a Gaussian distribution, and obtains the error that becomes zero. SNR threshold (threshold), by using a linear programming method to search for the optimal population of the weight of the row and the weight of the column, so that the Gaussian noise becomes the maximum when the encoding rate is fixed, and the check matrix for the LDPC code is generated according to the population .

Systems and methods for generating check node updates in the decoding of low-density parity-check (LDPC) codes use new approximations in order to reduce the complexity of implementing a LDPC decoder, while maintaining accuracy. The new approximations approximate the standard float-point sum-product algorithm (SPA), and can reduce the approximation error of min-sum algorithm (MSA) and have almost the same performance under 5 bits fix-point realization as the float-point sum-product algorithm (SPA).

The invention provides an iterative massage passing (IMP) PN code capturing method based on a sum-product algorithm on the condition of a low signal to noise ratio. According to the method, an m sequence is modelized into a special linear block code; a sum-product decoding algorithm is executed on a Tanner graph to obtain maximum posteriori estimation of the m sequence; and an initial state is selected by introducing a posteriori log-likelihood ratio to generate a local m sequence; and at last, serial correlation verification is carried out with reception data and capturing is completed. The greatest advantage of the method provided in the invention is as follows: maximum posteriori estimation of an m sequence is directly decoded by utilization of a sum-product decoding algorithm and a posteriori log-likelihood ratio is introduced to select an initial state. Besides, the method has advantages of rapid capturing speed, low complexity and excellent performance under a low signal to noise ratio. According to the invention, a fundamental principle on IMP PN code capturing is mainly introduced and then an IMP PN code capturing method based on a sum-product algorithm is provided; and an analysis on a selection process of an initial state of an m sequence is emphasized and a detailed flow chart is provided; at last, a capturing performance and a complexity of the method are analyzed though examples.

A low-density parity check (LDPC) code decoding method may be provided. The LDPC code decoding method may linearize or perform step-approximation on a natural logarithm hyperbolic cosine function included in a check node updating equation of a sum-product algorithm used for decoding an LDPC code, and may convert the linearized function to correspond to a check node updating equation of a min-sum algorithm.

Techniques are described for performing a check node update (CNU) as part of iterative decoding of a low density-parity check (LDPC) code. The CNU uses a min-sum decoding approach that monitors whether two values received in messages from two variable nodes connected to a check nodes are equal and are the minimum value among the values received by the check nodes from other variable nodes connected thereto. Upon detecting such an event, the minimum value is adjusted by reducing it by an adjustment value to generate an adjusted minimum value. This adjusted minimum value approximates the minimum value that a sum-product algorithm (SPA) decoding approach would have generated. The adjusted minimum value is included in a response message sent from the check node to a variable node. The bit corresponding to that variable node is decoded based on this adjusted minimum value.