G
Guido Montorsi
Researcher at Polytechnic University of Turin
Publications - 193
Citations - 7930
Guido Montorsi is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Turbo code & Convolutional code. The author has an hindex of 37, co-authored 192 publications receiving 7773 citations. Previous affiliations of Guido Montorsi include Huawei & Instituto Politécnico Nacional.
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Serial concatenation of interleaved codes: performance analysis, design and iterative decoding
TL;DR: In this article, the authors derived upper bounds to the average maximum likelihood bit error probability of serially concatenated block and convolutional codes with interleaver, and derived design guidelines for the outer and inner encoders that maximize the interleavers gain and the asymptotic slope of the error probability curves.
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Unveiling turbo codes: some results on parallel concatenated coding schemes
Sergio Benedetto,Guido Montorsi +1 more
TL;DR: A method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length is proposed and used to shed some light on some crucial questions which have been floating around in the communications community since the proposal of turbo codes.
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A soft-input soft-output APP module for iterative decoding of concatenated codes
TL;DR: This letter describes the SISO APP module that updates the APP corresponding to the input and the output bits, of a code, and shows how to embed it into an iterative decoder for a new hybrid concatenation of three codes, to fully exploit the benefits of the proposed S ISO APP module.
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Design of parallel concatenated convolutional codes
Sergio Benedetto,Guido Montorsi +1 more
TL;DR: The separate contributions that the interleaver length and constituent codes give to the overall performance of the parallel concatenated code are characterized, and some guidelines for the optimal design of the constituent convolutional codes are presented.
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Soft‐input soft‐output modules for the construction and distributed iterative decoding of code networks
TL;DR: Soft-input soft-output building blocks (modules) are presented to construct and iteratively decode in a distributed fashion code networks, a new concept that includes, and generalizes, various forms of concatenated coding schemes.