Design and Evaluation of Adaptive (Serial/Parallel) Concatenated Convolutional Codes

Abstract

In this paper, parallel Concatenated Convolutional Codes (PCCCs) is modeled as a special case of Serial Concatenated Convolutional Code (SCCCs). Consequently, resulting in Adaptive (parallel/serial) concatenated convolutional code in which the same encoder
and decoder can be used for both types of concatenated convolutional codes. To achieve this goal some interleaver restrictions are made to modify the classical structure of Semi-Random interleaver. The decoding stage is based on classical SCCC iterative decoder,
with added interleaver restrictions. The core of decoder structure is a soft-input soft-output (SISO) a posteriori probability (APP) module. Log-Map is used as (APP) for its superior performance. This work also presents some classical structured interleavers such as Block and Circular interleavers, adding to them the presented restrictions for sake of comparison. The resulted performance curves from computer simulation shows that degradation signal per bit energy to noise ratio (Eb/No) for the proposed decoder as compared to the classical PCCC is no more than 0.45 dB at less than 10-5 bit error rates (BER),over Fading channel in worst case.