  Decoding of convolutional codes poses a significant challenge for coding
theory. Classical methods, based on e.g. Viterbi decoding, suffer from being
computationally expensive and are restricted therefore to codes of small
complexity. Based on analogies with model predictive optimal control, we
propose a new iterative method for convolutional decoding that is cheaper to
implement than established algorithms, while still offering significant error
correction capabilities. The algorithm is particularly well-suited for decoding
special types of convolutional codes, such as e.g. cyclic convolutional codes.
