We present a new class of iteratively decodable turbo-like codes, called braided convolutional codes. Constructions and encoding procedures for tightly and sparsely braided convolutional codes are introduced. Sparsely braided codes exhibit good convergence behavior with iterative decoding, and a statistical analysis using Markov permutors shows that the free distance of these codes grows linearly with constraint length, i.e., they are asymptotically good.

Multiple turbo codes (MTC's) have been shown to be capable of achieving better performance than conventional turbo codes. Most previous research on MTC's, however, has been focused on low rate schemes. In this paper, we present a systematic approach to designing high rate MTC's. Several MTC's with low complexity constituent encoders are considered as examples. First, a simple method is presented to jointly design multiple dithered relative prime interleavers. Then, based on the extrinsic information transfer characteristics of the constituent encoders, good periodic puncturing patterns are obtained using a sequential search algorithm. We compare the resulting high rate MTC's with those obtained using an alternative optimized random puncturing pattern. Simulation results show that the new codes designs, for rates up to 3/4, exhibit good performance in the waterfall region of the frame error rate (FER) curve, without any sign of an error floor down to FER's as low as 10-5

We present a new class of iteratively decodable turbo-like codes, called braided convolutional codes. Constructions and encoding procedures for tightly and sparsely braided codes are introduced. Sparsely braided codes exhibit good convergence behavior with iterative decoding, and a statistical analysis using Markov permutors shows that the free distance of these codes grows linearly with constraint length.