A new class of parallel data convolutional codes

We propose a new class of parallel data convolutional codes (PDCCs) in this paper. The PDCC encoders inputs are composed of an original block of data and its interleaved version. A novel single self-iterative soft-in/soft-out a posteriori probability (APP) decoder structure is proposed for the decoding of the PDCCs. Simulation results are presented to compare the performance of PDCCs to that of parallel concatenated convolutional codes (PCCCs).