A Bidirectional Decoding Architecture for Double Binary Convolutional Turbo Code Based on Symmetry

This paper presents a bidirectional decoding architecture for double binary convolutional turbo code specified in IEEE 802.16m standard to accelerate its decoding speed. The branch metrics matrix is defined to decrease the decoding computational complexity. Furthermore, the forward factor matrix and backward factor matrix are defined, and then the symmetrical property between them is discussed and applied to the bidirectional calculation of forward metrics, backward metrics and a posteriori log-likelihood ratio. Detailed analyses are presented in terms of computational complexity, memory size, decoding speed and bit error rate, which show that computational complexity and memory size of the proposed architecture are not increased, while the decoding speed is one time accelerated as compared with the conventional scheme.