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Sequential decoding
About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.
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TL;DR: Algorithms to decode iterated codes when at least one of the "component codes" is majority decodable are given and all error patterns guaranteed to be correctable by the minimum distance of the iterated code.
Abstract: Algorithms to decode iterated codes when at least one of the "component codes" is majority decodable are given. The decoding algorithms allow the use of the decoders of the component codes and still make it possible to correct all error patterns guaranteed to be correctable by the minimum distance of the iterated code.
33 citations
TL;DR: A new block convolutional code, based on the turbo code principle, is presented, which does not require any trellis termination and can be decoded with a single soft-output Viterbi decoder, for low and medium data rates.
Abstract: A new block convolutional code, based on the turbo code principle, is presented. This code does not require any trellis termination and can be decoded with a single soft-output Viterbi decoder, for low and medium data rates. Some simulation results are given for an AWGN channel.
33 citations
TL;DR: By construction, this paper shows by construction the existence of convolutional codes that are both strongly-MDS and MDP for all choices of parameters.
Abstract: This paper revisits strongly-MDS convolutional codes with maximum distance profile (MDP). These are (non-binary) convolutional codes that have an optimum sequence of column distances and attains the generalized Singleton bound at the earliest possible time frame. These properties make these convolutional codes applicable over the erasure channel, since they are able to correct a large number of erasures per time interval. The existence of these codes have been shown only for some specific cases. This paper shows by construction the existence of convolutional codes that are both strongly-MDS and MDP for all choices of parameters.
33 citations
10 Jun 2012
TL;DR: A new construction of punctured-node protograph-based Raptor-like (PN-PBRL) codes that is suitable for long-blocklength applications, and as with the Raptor codes, additional parity bits can be easily produced by exclusive-OR operations on the precoded bits, providing extensive rate compatibility.
Abstract: This paper presents a new construction of punctured-node protograph-based Raptor-like (PN-PBRL) codes that is suitable for long-blocklength applications. As with the Raptor codes, additional parity bits can be easily produced by exclusive-OR operations on the precoded bits, providing extensive rate compatibility. The new construction provides low iterative decoding thresholds that are within 0.45 dB of the capacity for all code rates studied, and the construction is suitable for long blocklengths. Comparing at the same information block size of k = 16368 bits, the PN-PBRL codes are as good as the best known AR4JA codes in the waterfall region. The PN-PBRL codes also perform comparably to DVB-S2 LDPC codes even though the DVB-S2 codes have longer blocklength and outer BCH codes.
33 citations
AT&T1
TL;DR: It is shown that the experimental results agree well with those from theory, and that trellis coding provides significant improvement in performance in the presence of impairments other than additive Gaussian noise.
Abstract: We describe the application of multilevel trellis coding to the transmission of high-speed' voiceband data. The implementation aspects of soft-decision Viterbi decoding associated with such coding are discussed, Real-time performance comparison is made of two coded systems at 9600 bits/s with respect to an uncoded system. The performance sensitivity Of the three systems to DDD channel impairments other than additive Gaussian noise are also described. It is shown that the experimental results agree well with those from theory, and that trellis coding provides significant improvement in performance in the presence of impairments other than additive Gaussian noise.
33 citations