A Novel Parallel Concatenated Convolutional Code Structure Based on Frame Decomposition

This paper introduces a novel decomposed parallel concatenated convolutional code (DPCCC) structure designed to increase the minimum Hamming distance (mHD) and reduce the error floor compared to conventional PCCC structures, such as turbo codes (TCs). In DPCCC, the input frame is partitioned into subframes, each independently interleaved and encoded. This decomposition targets mHD codewords, partly through mitigating the quadratic increase in the multiplicity of periodic input weight-2 sequences, thus lowering their probability of co-occurrence in the component codes. We also propose a design algorithm that optimizes each subframe interleaver while accounting for inter-frame dependencies. Simulation results demonstrate that the proposed DPCCC structure, combined with the tailored interleaver design, achieves competitive performance compared to other code classes, with notable improvements in mHD and error floor performance over TCs, while maintaining rate compatibility and supporting low-latency decoder architectures.