A Based Algorithm for Reduced Complexity ML Decoding of Tailbiting Codes

• Published in: IEEE communications letters Vol. 14; no. 9; pp. 854 - 856
• Main Authors: Ortin, Jorge, Garcia, Paloma, Gutierrez, Fernando, Valdovinos, Antonio
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: A algorithm; Algorithms; Applied sciences; Coding, codes; Complexity; Computation; Computational complexity; Computational modeling; Convolutional codes; Decoding; Exact sciences and technology; Government; Heuristic; IA algorithm; Information, signal and communications theory; Land mobile radio cellular systems; Mathematical analysis; Mathematical models; Maximum likelihood decoding; Signal and communications theory; Stopping; Tail; tailbiting; Telecommunications and information theory; Termination of employment; Viterbi algorithm; Viterbi decoding; WiMAX; A algorithm; Convolutional codes; Convolutional code; Computational complexity; decoding; Search algorithm; Maximum likelihood decoding; tailbiting; Simulation; Algorithm complexity; Heuristic method; Viterbi decoding; IA algorithm
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2010.072310.100295

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.