Artificial neural network viterbi decoding system and method

Claim 2

A maximum likelihood decoding system for high speed and efficient decoding of convolutionally-encoded data streams of constraint lengths greater than 6, comprising an analog neural network having a plurality of analog neurons, said analog neurons for mathematically combining analog signals, said analog neural network adapted to receive a convolutionally-encoded digital data stream and adapted to decode said data stream using said analog neurons into a sequence of digital values by performing a Viterbi algorithm, said analog network minimizing error in said digital values.

Claim 3

The system of claim 1, wherein said analog neural network comprises an analog means for determining a maximum likelihood code word corresponding with said digital data stream based upon path metrics associated with paths in said analog neural network.

Claim 4

The system of claim 1, further comprising a digital means connected to said analog neural network, said digital means for storing a digital information sequence and for updating said information sequence.

Claim 5

The system of claim 1, further comprising an analog means connected to said analog neural network, said analog means for storing a digital information sequence and for updating said information sequence.

Claim 6

The system of claim 1, further comprising a means for identifying a minimum path metric among various states and for reducing path metrics by a proportional amount.

Claim 7

The system of claim 1, further comprising a digital shift register using analog neurons.

Claim 8

The system of claim 1, further comprising a branch metric computer for processing analog metric values associated with neurons in said analog neural network.

Claim 9

The system of claim 1 situated within an integrated circuit.

Claim 10

The system of claim 1, wherein said plurality of analog neurons include threshold-limiting neurons and hard-limiting neurons.

Claim 11

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.

Claim 12

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.

Claim 13

The system of claim 10, wherein said register exchange means comprises a digital means for storing a digital information sequence and for updating said information sequence.

Claim 14

The system of claim 10, wherein said register exchange means comprises an analog means for storing a digital information sequence and for updating said information sequence.

Claim 15

The system of claim 10, further comprising a means in communication with said second and fourth neurons, said means for identifying a minimum partial path metric output from said fourth neurons and for reducing partial path metrics by an amount corresponding with said minimum partial path metric at said second neurons.

Claim 16

The system of claim 10 wherein said output register selection means comprises a digital shift register comprising analog neurons.

Claim 17

The system of claim 10 implemented within an integrated circuit.

Claim 18

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.

Claim 19

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.

Claim 24

The method of claim 18, further comprising the step of using a trellis defined by said first, second, and third neurons having a decoding depth which is sufficiently large so that said largest magnitudes are selected arbitrarily from said current surviving partial path metrics.