Abstract: Decoding signals represented by a trellis of a block length divided into windows includes a step of decoding a portion of the trellis using backward recursion starting from a point that is after the end of a window backwards to the end of the window, defining a learning period, to determine a known state metric at the end of the window. A length of the learning period for each window dependents on the signal quality such that a shorter learning period is chosen for a higher signal quality. The signal quality used is an intrinsic signal-to-noise ratio derived from the log-likelihood-ratio of the soft outputs of the decoded window. In particular, the intrinsic signal-to-noise ratio of the signal is defined as a summation of generated extrinsic information multiplied by a log-likelihood-ratio (LLR) value at each iteration.

Abstract: A decoder dynamically terminates iteration calculations in the decoding of a received convolutionally coded signal using local quality index criteria. In a turbo decoder with two recursion processors connected in an iterative loop, at least one additional recursion processor is coupled in parallel at the inputs of at least one of the recursion processors. All of the recursion processors perform concurrent iterative calculations on the signal. The at least one additional recursion processor calculates a local quality index of a moving average of extrinsic information for each iteration over a portion of the signal. A controller terminates the iterations when the measure of the local quality index is less than a predetermined threshold, and requests a retransmission of the portion of the signal.