Developing the principle of divergent coding for Gaussian channels

Mutithreshold decoding for convolutional codes implementing optimization methods to correct errors based on the search of global extremum of functionals in discrete spaces are considered. The implementation of divergent coding methods with triple concatenation to achieve highly efficient decoding near channel capacity is described. The possibilities of this approach while using concatenation are shown. Multialgorithmic decoding is offered. It implements the principle of divergence with simultaneous usage of two types of decoders: Viterbi algorithm and multithreshold decoding. The implementation of multialgorithmic decoding is shown to substantially decrease decoding complexity at low signal to noise ratio.