Complexity-Reduced Iterative Source-Channel Decoding Using Inner Irregular Insertion Convolutional Codes

Abstract:
Iterative source-channel decoding (ISCD) might be computationally expensive depending on the system configuration. This is a crucial factor in many applications due to energy constraints. Therefore, complexity-reduction is an important issue. In this paper, we propose an ISCD system using inner irregular insertion convolutional (IC) codes that inserts known dummy bits into the data frame before convolutional encoding to exploit them as perfect a priori knowledge at the related MAP decoder. Complexity-reduction is primarily achieved for the soft decision source decoder (SDSD) by decreasing the number of parity bits of the redundant bit mapping (RBM) corresponding to the fraction of dummy bits.