Fundamentals of Convolutional Coding
Fundamentals of Convolutional Coding
Encoder and Distance Properties of Woven Convolutional Codes with One Tailbiting Component Code
Problems of Information Transmission
Active distances for convolutional codes
IEEE Transactions on Information Theory
Minimal tail-biting trellises: the Golay code and more
IEEE Transactions on Information Theory
Optimal and near-optimal encoders for short and moderate-length tail-biting trellises
IEEE Transactions on Information Theory
Tailbiting codes: bounds and search results
IEEE Transactions on Information Theory
Woven convolutional codes .I. Encoder properties
IEEE Transactions on Information Theory
Tailbiting codes obtained via convolutional codes with large active distance-slopes
IEEE Transactions on Information Theory
A Tightened Upper Bound on the Error Probability of Binary Convolutional Codes with Viterbi Decoding
IEEE Transactions on Information Theory
Encoder and Distance Properties of Woven Convolutional Codes with One Tailbiting Component Code
Problems of Information Transmission
Distance Approach to Window Decoding
Problems of Information Transmission
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The error-correcting capability of tailbiting codes generated by convolutional encoders is described. In order to obtain a description beyond what the minimum distance dmin of the tailbiting code implies, the active tailbiting segment distance is introduced. The description of correctable error patterns via active distances leads to an upper bound on the decoding block error probability of tailbiting codes. The necessary length of a tailbiting code so that its minimum distance is equal to the free distance dfree of the convolutional code encoded by the same encoder is easily obtained from the active tailbiting segment distance. This is useful when designing and analyzing concatenated convolutional codes with component codes that are terminated using the tailbiting method. Lower bounds on the active tailbiting segment distance and an upper bound on the ratio between the tailbiting length and memory of the convolutional generator matrix such that dmin equals dfree are derived. Furthermore, affine lower bounds on the active tailbiting segment distance suggest that good tailbiting codes are generated by convolutional encoders with large active-distance slopes.