Abstract
In this paper we present an efficient algorithm to decode linear block codes on binary channels. The main idea consists in using a vote procedure in order to elaborate artificial reliabilities of the binary received word and to present the obtained real vector r as inputs of a SIHO decoder (Soft In/Hard Out). The goal of the latter is to try to find the closest codeword to r in terms of the Euclidean distance. A comparison of the proposed algorithm over the AWGN channel with the Majority logic decoder, Berlekamp-Massey, Bit Flipping, Hartman-Rudolf algorithms and others show that it is more efficient in terms of performance. The complexity of the proposed decoder depends on the weight of the error to decode, on the code structure and also on the used SIHO decoder.
Highlights
The current large development and deployment of wireless and digital communication encourages the research activities in the field of error correcting codes
In this paper we present an efficient algorithm to decode linear block codes on binary channels
In this paper we have presented an efficient hard decision decoding algorithm which uses the dual space of a linear code C to compute artificial reliabilities of binary the received word h by a majority voting procedure
Summary
The current large development and deployment of wireless and digital communication encourages the research activities in the field of error correcting codes. Codes are used to improve the reliability of data transmitted over communication channels susceptible to noise. Coding techniques create codewords by adding redundant information to the user information. There are two classes of error correcting codes: convolutional codes and block codes. The class of block codes contains two subclasses: nonlinear codes and linear codes. The principle of a block code C(n, k) is as follows: the initial message is cut out into blocks of length k. The length of the redundancy is n – k and the length of transmitted blocks is n. If the code C is linear the code C (n, n – k) defined by (1) is linear with “.” denotes the scalar (dot) product
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