5 results about "Ratio Sequence" patented technology

The invention discloses a finite geometric code decoding method and system based on a generalized check matrix, and relates to the communication technology, and the method comprises the following steps: constructing a corresponding generalized check matrix according to an algebraic structure of a multi-step large number logic decodable finite geometric code; sending a finite geometric code sequence, transmitting the sequence after coding and BPSK modulation, and receiving a posterior probability log-likelihood ratio sequence by a receiving end; and carrying out iterative decoding on the finite geometric code received by the receiving end based on the constructed generalized check matrix. According to the invention, a new generalized check matrix is constructed according to the algebraic structure of the finite geometric code, and multi-step large-number logic iterative decoding is carried out on the finite geometric code based on the matrix, so that the iterative decoding performance of the multi-step large-number logic decodable finite geometric code is improved.

The invention relates to the field of storage, in particular to a method for constructing a random write steady state of solid-state storage equipment and the solid-state storage equipment, the method is applied to the solid-state storage equipment, and the method comprises the following steps: carrying out primary segmentation on a user capacity region from the tail of the region according to the size X of a super block, obtaining a plurality of sub-intervals with the size of X and non-writing intervals with the size less than X; performing random writing on the plurality of sub-intervals; dividing each sub-interval in the plurality of sub-intervals into a user data area and a rewriting area except the user data area; wherein the sizes of the user data areas in each subinterval are in an equal-ratio sequence; forming a to-be-written interval list by the rewriting areas and the non-writing intervals in each sub-interval in the plurality of sub-intervals; and judging whether the data volume of the to-be-written interval list meets the size X of the super block, if so, carrying out segmentation, random writing and recombination on the to-be-written interval list again to obtain a new to-be-written interval list, and if not, carrying out random writing on the to-be-written interval list.

The application discloses a low-complexity forward-backward decoding method based on weighted edit distance, and the method comprises the following steps: b A code word sequence with a length of N L symbols is generated by an encoder of an LDPC code d A mark code w is uniformly inserted into the code word sequence d to generate a sending code word with a length of N c and output the sending code word x After the sending code word x passes through an insertion / deletion-substitution channel, a receiving sequence with a length of N y is generated; a watermark decoder decodes the receiving sequence y by using a low-complexity forward-backward decoding method and outputs a likelihood ratio sequence l ; and an LDPC decoder decodes the likelihood ratio sequence l and outputs the application stores the calculation result of an intermediate metric value in a lookup table, reduces the number of repeated calculations of the intermediate metric value, reduces the calculation complexity of the decoding algorithm and improves the decoding speed.

The invention provides a cross-scale optimal scale ratio selection method for a transient strong nonlinear scale model test, and belongs to the field of engineering structures. The problem of large similarity conversion error caused by theoretical inapplicability of the existing method is solved. The method comprises the following steps: based on a model component test of a classical similarity theory, introducing a first-order derivative relationship that a variable pi item changes along with a scale ratio, establishing a micro-grouping similarity model and carrying out Taylor expansion to obtain an iterative relationship that the variable pi item changes along with the scale ratio; setting a scale ratio sequence, and converting the iterative relationship into a parabola mapping model representing the dynamic behavior of the system; the method comprises the steps of determining a pervasive constant and a scale-free interval corresponding to the pervasive constant on the basis of a reformation group theory, selecting a cross-scale optimal scale ratio according to the number of iterations, carrying out a model test, obtaining a model change pi time sequence, and carrying out similarity conversion on a model test result to a prototype on the basis of a parabola mapping model and the reformation group theory. The method is used in the field of transient strong nonlinear system dynamic response analysis.

The application discloses a geometric constellation shaping joint coding modulation method based on deep learning, and belongs to the geometric constellation shaping method. By constructing an end-to-end system containing a deep learning modulator and a demodulator, a log-likelihood ratio calibration module is integrated at the end of the demodulator to output soft information matched with a decoder; a multi-objective joint loss function containing a cross-entropy loss, a generalized mutual information loss and a boundary constraint loss is determined; a phased training strategy of first freezing the calibration module for basic constellation shaping and then jointly fine-tuning is adopted; the trained modulator is deployed at a sending end for geometric constellation shaping mapping, and the demodulator is deployed at a receiving end to output a calibrated log-likelihood ratio sequence to the decoder. The application realizes deep collaboration of coding and modulation, and improves transmission reliability and spectral efficiency.