A Finite Soft Decision Decoding With Data Combining for Product Codes Using Convolutional Codes

Abstract

In this thesis, we propose a feasible combining rule for a decoding scheme of product codes with finite soft decision data. Since convolutional codes have been widely used in wireless communication systems due to good error correcting performance in finite soft decision decoding, we focus on convolutional codes as the horizontal codes of product codes so that the proposed decoding scheme can be practically applied. The proposed combining rules are based on the analysis of the summation and tanh-rule for log-likelihood ratios (LLR) of the received signal. As a result, we can improve the decoding performance of product codes using convolutional codes with little additional complexity, compared to single convolutional codes.