Design of Optimized Convolutional and Serially Concatenated Convolutional Codes in the Presence of A-priori Information

In this paper, we focus on the design of optimized binary convolutional codes (CCs) and serially concatenated convolutional codes (SCCCs) in the presence of a-priori information (API) at the receiver. For large signal-to-noise ratios (SNRs), we first propose a CC design criterion based on the minimization of a union bound on the bit error probability (BEP). In this case, relevant performance gains, with respect to previously proposed CCs, are obtained. These gains persist even in the presence of estimation errors on the API. Then, we apply the same union bound-based design criterion to SCCCs. Since the BEP of SCCCs is characterized by a typical waterfall shape, the proposed union bound-based design criterion is accurate only at large SNR, to estimate the BEP floor. In order to complement this analysis, we propose a density evolution-based approach to optimize the SCCC design in terms of minimization of the SNR of the "knee" of the BEP curve. The obtained simulation results show substantial gains with respect to previously proposed parallel concatenated convolutional coding (PCCCing) schemes optimized under the assumption of no API at the decoder. Moreover, in the presence of strong API the proposed SCCCs allow to approach the Shannon limit (SL) more than any previously proposed turbo coding scheme.