Artificial neural network viterbi decoding system and method

A decoding system for high speed and efficient decoding of convolutionally-encoded data streams utilizing an artificial neural network and the Viterbi algorithm, comprising:

• (a) a correlation computation means having first and second neurons, said first and second neurons being adapted to mathematically combine analog signals, said first neurons for receiving said convolutionally-encoded data stream and for generating branch metrics, said second neurons for mathematically combining said branch metrics from said first neurons with previous partial path metrics to derive current partial path metrics;
• (b) a surviving path selection means in communication with said correlation computation means, said surviving path selection means having third and fourth neurons, said third neurons for identifying surviving partial path metrics, said fourth neurons for providing said surviving partial path metrics to said second neurons, said surviving partial path metrics being used by said second neurons as previous partial path metrics during a subsequent time increment;
• (c) a maximum path metric selection means in communication with said surviving path selection means, said maximum path metric selection means for determining identities of largest surviving partial path metrics;
• (d) a register exchange means in communication with said surviving path selection means, said register exchange means for storing and updating the magnitudes of surviving partial path metrics;
• and
• (e) an output register selection means in communication with said maximum path metric selection means and said register exchange means, said output register selection means for providing a convolutionally-decoded output data stream with digital values corresponding with said surviving partial path metrics.

A decoding system for high speed and efficient decoding of convolutionally-encoded data streams utilizing an artificial neural network and the Viterbi algorithm, comprising:

• (a) a correlation computation means having first and second neurons, said first neurons for receiving said convolutionally-encoded data stream and for generating branch metrics, said second neurons for mathematically combining said branch metrics from said first neurons with previous partial path metrics to derive current partial path metrics;
• (b) a surviving path selection means in communication with said correlation computation means, said surviving path selection means having third and fourth neurons, said third neurons for identifying surviving partial path metrics, said fourth neurons for providing said surviving partial path metrics to said second neurons, said surviving partial path metrics being used by said second neurons as previous partial path metrics during a subsequent time increment;
• (c) a register exchange means in communication with said surviving path selection means, said register exchange means for storing and updating magnitudes of said surviving partial path metrics;
• (d) an output register selection means in communication with said register exchange means, said output register selection means for providing a convolutionally-decoded output data stream with digital values corresponding with said surviving partial path metrics;
• and
• wherein a decoding depth associated with a trellis defined by said first, second, and third neurons is sufficiently large so that a magnitude of any one of said surviving partial path metrics output from said register exchange means is selected for output by said output register selection means.

A method for high speed and efficient decoding of convolutionally-encoded data streams utilizing a neural network and the Viterbi algorithm, comprising:

• receiving the convolutionally-encoded data stream;
• generating branch metrics by passing said data stream through first neurons:
• mathematically combining at third neurons said branch metrics from said first neurons and said previous surviving partial path metrics from said second neurons;
• comparing at fourth neurons potential surviving partial path metrics output from said third neurons;
• determining identities at said fourth neurons of current surviving partial path metrics from said comparisons of said potential surviving partial path metrics;
• determining largest magnitudes of said current surviving partial path metrics;
• and
• providing a convolutionally-decoded output data stream having a sequence of digital values corresponding with predetermined states corresponding with said current surviving partial path metrics, thereby minimizing error in said digital values.