Based on the two-dimensional (2-D) weight distribution of tail-biting codes we give guidelines on how to choose tail biting component codes that are especially suited for parallel concatenated coding schemes. Employing these guidelines, we tabulate tail-biting codes of different rate, length, and complexity. The performance of parallel concatenated block codes (PCBCs) using iterative (turbo) decoding is evaluated by simulation and bounds are calculated in order to study their asymptotic performance.

The Golay convolutional code and related block codes are investigated from an application point of view. We calculate the distance spectrum of the Golay convolutional code and the weight distributions of the block codes using the transfer-matrix method. The performance of the considered codes is shown and discussed. Moreover, the codes are applied as component codes of parallel concatenated codes which are iteratively decoded.

A symbol-by-symbol maximum a posteriori (MAP) decoding algorithm for high-rate convolutional codes applying reciprocal dual convolutional codes is presented. The advantage of this approach is a reduction of the computational complexity since the number of codewords to consider is decreased. All requirements for iterative decoding schemes are fulfilled. Since tail-biting convolutional codes are equivalent to quasi-cyclic block codes, the decoding algorithm for truncated or terminated convolutional codes is modified to obtain a soft-in/soft-out decoder for high-rate quasi-cyclic block codes which also uses the dual code because of complexity reasons. Additionally, quasi-cyclic block codes are investigated as component codes for parallel concatenation. Simulation results obtained by iterative decoding are compared with union bounds for maximum likelihood decoding. The results of a search for high-rate quasi-cyclic block codes are given in the appendix.

A new symbol-by-symbol maximum a posteriori (MAP) decoding algorithm for high-rate convolutional codes using reciprocal dual convolutional codes is presented. The advantage of this approach is a reduction of the computational complexity since the number of codewords to consider is decreased for codes of rate greater than 1/2. The discussed algorithms fulfil all requirements for iterative ("turbo") decoding schemes. Simulation results are presented for high-rate parallel concatenated convolutional codes ("turbo" codes) using an AWGN channel or a perfectly interleaved Rayleigh fading channel. It is shown that iterative decoding of high-rate codes results in high-gain, moderate-complexity coding.