Editing Turbo code (section)

==Solving hypotheses to find bits==
The key innovation of turbo codes is how they use the likelihood data to reconcile differences between the two decoders.  Each of the two convolutional decoders generates a hypothesis (with derived likelihoods) for the pattern of ''m'' bits in the payload sub-block.  The hypothesis bit-patterns are compared, and if they differ, the decoders exchange the derived likelihoods they have for each bit in the hypotheses.  Each decoder incorporates the derived likelihood estimates from the other decoder to generate a new hypothesis for the bits in the payload.  Then they compare these new hypotheses.  This iterative process continues until the two decoders come up with the same hypothesis for the ''m''-bit pattern of the payload, typically in 15 to 18 cycles.

An analogy can be drawn between this process and that of solving cross-reference puzzles like [[crossword]] or [[sudoku]].  Consider a partially completed, possibly garbled crossword puzzle.  Two puzzle solvers (decoders) are trying to solve it:  one possessing only the "down" clues (parity bits), and the other possessing only the "across" clues.  To start, both solvers guess the answers (hypotheses) to their own clues, noting down how confident they are in each letter (payload bit).  Then, they compare notes, by exchanging answers and confidence ratings with each other, noticing where and how they differ.  Based on this new knowledge, they both come up with updated answers and confidence ratings, repeating the whole process until they converge to the same solution.