Analysis of decoders for convolutional codes by stochastic sequential machine methods
Convolutional code decoder modeled as autonomous stochastic sequential machine, considering finite Markov chain theory for error probability
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Convolutional code decoder modeled as autonomous stochastic sequential machine, considering finite Markov chain theory for error probability
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Rate 1/2 convolutional error correcting binary codes with complementary generators, discussing synthesis and search procedure for largest free distance
Description of a class of rate 1/2 nonsystematic convolutional codes with the following desirable properties: (1) an undetected decoding error probability verified by simulation to be much smaller than for the best systematic codes of the same constraint length; (2) computation behavior with sequential decoding verified by simulation to be virtually identical to that of the best systematic codes; (3) a ?quick-look-in' feature that permits recovery of the information sequence from the hard-decisioned received data without decoding simply by modulo-two addition of the received sequences; and (4) suitability for encoding by simple circuitry requiring less hardware than encoders for the best systematic codes of the same constraint length. Theoretical analyses are given. These codes have been adopted for use in several forthcoming space missions.
A simple branch synchronizer for rate 1/3, constraint length five, nonsystematic convolutional code is described. The scheme derives the sync information from the received channel bits, thus avoiding the extra formatting of the sync data at the transmitter and receiver, and increasing the data rate. The synchronizer detects the proper sync bits for both the in-phase and phase ambiguous cases. The design is adaptable to nonsystematic codes of different constraint lengths.
A recursive procedure is derived for decoding of rate R=1/n binary convolutional codes which minimizes the probability of the individual decoding decisions for each information bit subject to the constraint that the decoding delay be limited to Delta branches. This new decoding algorithm is similar to, but somewhat more complex than, the Viterbi decoding algorithm. A real-time, i.e. fixed decoding delay, version of the Viterbi algorithm is also developed and used for comparison to the new algorithm on simulated channels. It is shown that the new algorithm offers advantages over Viterbi decoding in soft-decision applications such as in the inner coding system for concatenated coding.
A recursive procedure is derived for decoding of rate R = 1/n binary convolutional codes which minimizes the probability of the individual decoding decisions for each information bit, subject to the constraint that the decoding delay be limited to Delta branches. This new decoding algorithm is similar to, but somewhat more complex than, the Viterbi decoding algorithm. A real-time, i.e., fixed decoding delay, version of the Viterbi algorithm is also developed and used for comparison to the new algorithm on simulated channels. It is shown that the new algorithm offers advantages over Viterbi decoding in soft-decision applications, such as in the inner coding system for concatenated coding.