Gear-shift decoding for algorithms with varying complexity

We consider an iterative message-passing decoder that can choose its decoding rule among a group of decoding algorithms at each iteration (for example: a software decoder). Each available decoding algorithm may have a different computation time and performance. We first show that with proper choice of algorithm at each iteration, decoding latency can significantly be reduced. We call such a decoder a gear-shift decoder because it changes its decoding rule (shifts gear) in order to guarantee both convergence and minimum decoding-latency. We also prove that the optimum gear-shift decoder (the one with the minimum decoding-latency) has a decoding threshold equal to or better than the best decoding threshold of the available algorithms. We use extrinsic information transfer charts and dynamic programming to find the optimum gear-shift decoder.