Hybrid Maximum Likelihood Decoding for Linear Block Codes

In this paper we propose a hybrid maximum likelihood decoding (MLD) for linear block codes. For the reliable data transmission over noisy channels, convolutional and block codes are widely used in most digital communication systems. Much more efficient algorithms have been found for using channel measurement information in the decoding of convolutional codes than in the decoding of block codes. Word correlation method can be utilized to use channel measurement information in the decoding of block codes. However as the number of code words becomes larger, the decoding complexity increases dramatically to the power of the number of information bits. The hybrid maximum likelihood decoding can solve the problem of the hardware complexity as well as the computational time. Simulation result for Reed Muller code is presented to demonstrate the effectiveness of the algorithm.