Comparison of Low Complexity Fast Iterative Decoding Techniques for Convolutional Self-Doubly-Orthogonal Codes

A class of orthogonal convolutional codes featuring self-doubly-orthogonal properties is analyzed under iterative decoding techniques. The self-doubly-orthogonal properties of these codes allow them to approach the asymptotic error performance using a low complexity iterative threshold decoding algorithm. It can be shown that convolutional self-doubly-orthogonal codes are also suited for iterative belief propagation (BP) decoding algorithm with typically five iterations to approach the asymptotic error performance. At a substantially reduced complexity, iterative threshold decoding requires the same number of iterations as iterative BP decoding to achieve practically the asymptotic error performance at moderate signal-to-noise ratios.