A* Based Algorithm for Reduced Complexity ML Decoding of Tailbiting Codes

TL;DR

This paper presents an improved A* algorithm for tailbiting code decoding that reduces computational complexity by utilizing initial Viterbi decoding information for a more accurate heuristic and early termination.

Contribution

The work introduces modifications to the A* decoding process that leverage initial Viterbi results to enhance heuristic accuracy and reduce complexity.

Findings

Complexity decreased in terms of operations performed

Heuristic accuracy improved with initial Viterbi information

Early termination conditions reduce decoding time

Abstract

The A* algorithm is a graph search algorithm which has shown good results in terms of computational complexity for Maximum Likelihood (ML) decoding of tailbiting convolutional codes. The decoding of tailbiting codes with this algorithm is performed in two phases. In the first phase, a typical Viterbi decoding is employed to collect information regarding the trellis. The A* algorithm is then applied in the second phase, using the information obtained in the first one to calculate the heuristic function. The improvements proposed in this work decrease the computational complexity of the A* algorithm using further information from the first phase of the algorithm. This information is used for obtaining a more accurate heuristic function and finding early terminating conditions for the A* algorithm. Simulation results show that the proposed modifications decrease the complexity of ML decoding with the A* algorithm in terms of the performed number of operations.