Encoding method and apparatus

Abstract

The present invention provides an encoding method and apparatus. The method includes the following steps that: N to-be-encoded bits are determined, wherein the N to-be-encoded bits include informationbits and fixed bits, and N is a positive integer; first polarization weight vectors which contain the polarization weights of N polarization channels are obtained, wherein the polarization weights isa basis for selecting the information bits, and the N polarization channels are corresponding to the N to-be-encoded bits; and the positions of the information bits are determined according to the first polarization weight vectors; and Polar-encoding is performed on the N to-be-encoded bits, so that Polar-encoded bits can be obtained. With the encoding method and apparatus provided by the presentinvention adopted, the computational complexity and storage complexity of the encoding of Polar codes can be decreased.

Description

technical field [0001] The present invention relates to the technical field of encoding and decoding, in particular to an encoding method and equipment. Background technique [0002] Channel coding is a wireless access technology used in communication systems to improve the reliability of data transmission and ensure communication quality. Polar (polarization) code is a coding method that is theoretically proven to reach the Shannon limit and has low coding and decoding complexity. . Polar code is a linear block code whose generator matrix is ​​G N , its encoding process is is encoded bits, is the bits to be coded, where Is a binary row vector with a length of N (that is, the code length); G N is an N×N matrix, and in B N is an N×N transposition matrix, such as a bit reversal (Bit Reversal) matrix; defined as matrix F 2 The Kronecker product of . In the encoding process of the Polar code, it is first necessary to determine The position of the informati...

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Problems solved by technology

[0003] In the prior art, there are three methods for estimating the reliability of polarized channels. One is to calculate the Bhattacharyya parameter of each polarized channel, which reflects the error probability of the polarized channel, and then select the K smallest The polarization channel corresponding to the Bhattachary parameter places information bits, but this method is only applicable to the binary erasure channel, and the reliability cannot be accurately estimated for other channels, so the performance is not high; the other two are density evolution (Density evolution for short) : DE) method and Gaussian approximation (Gaussion approximation, referred to as: GA) method

The calculation complexity of DE method and GA method is relatively high, which is not suitable for online calculation. For offline storage, DE method and GA method depend on the parameter rate matching method, code rate and modulation method of the actual channel when calculating reliability. If any of the above parameters changes, the reliability estimation result of the polarized channel will also change, and the position of the corresponding information bit will also change, which will eventually lead to excessive storage overhead

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