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Journal ArticleDOI

A new multilevel coding method using error-correcting codes

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TLDR
A new multilevel coding method that uses several error-correcting codes that makes effective use of soft-decisions to improve the performance of decoding and is superior to other multileVEL coding systems.
Abstract
A new multilevel coding method that uses several error-correcting codes is proposed. The transmission symbols are constructed by combining symbols of codewords of these codes. Usually, these codes are binary error-correcting codes and have different error-correcting capabilities. For various channels, efficient systems can be obtained by choosing these codes appropriately. Encoding and decoding procedures for this method are relatively simple compared with those of other multilevel coding methods. In addition, this method makes effective use of soft-decisions to improve the performance of decoding. The decoding error probability is analyzed for multiphase modulation, and numerical comparisons to other multilevel coding systems are made. When equally complex systems are compared, the new system is superior to other multilevel coding systems.

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Citations
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Journal ArticleDOI

On nested convolutional codes and their application to woven codes

TL;DR: A method to construct the set of all minimal (rational) generator matrices of a given convolutional code is presented and the free-distance profile of the inner generator matrix is shown to be an important design tool.
Proceedings ArticleDOI

Iterative phase recovery method of turbo-coded 16QAM based on soft symbol to bit mapping

TL;DR: A new binary turbo-coded system with phase recovery in I6-QAM modulation, based on the soft symbol to bit mapping technique, which gets the soft bit information from the received signals and sends them to the binary turbo decoders.
Book ChapterDOI

Low-complexity and high-performance multilevel coded modulation for the AWGN and Rayleigh fading channels

TL;DR: The multilevel coding method is a powerful technique for constructing bandwidth efficient modulation codes systematically with arbitrarily large distance parameters from Hamming distance component codes in conjunction with proper bitsto-signal mapping through signal-set partitioning.
Posted Content

Coupling Data Transmission for Multiple-Access Communications

TL;DR: It is proved that a two-stage demodulation/decoding method, in which iterative demodulating based on symbol estimation and interference cancellation is followed by parallel error-correction decoding, achieves capacity on the additive white Gaussian noise channel asymptotically.
Patent

Transmission apparatus, reception apparatus and communication system

TL;DR: In this paper, the performance of a decoder is improved by inserting a doping bit sequence, which is known between the transmission apparatus and reception apparatus, into the information bit sequence to be transmitted.
References
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Journal ArticleDOI

Convolutional Codes and Their Performance in Communication Systems

TL;DR: This tutorial paper begins with an elementary presentation of the fundamental properties and structure of convolutional codes and proceeds with the development of the maximum likelihood decoder, which yields for arbitrary codes both the distance properties and upper bounds on the bit error probability.
Journal ArticleDOI

Short convolutional codes with maximal free distance for rates 1/2, 1/3, and 1/4 (Corresp.)

TL;DR: In this paper, the authors gave a tabulation of binary convolutional codes with maximum free distance for rates of 1/2, 1/3, and 1/4 for all constraint lengths up to and including nu = 14.
Journal ArticleDOI

Short binary convolutional codes with maximal free distance for rates 2/3 and 3/4 (Corresp.)

TL;DR: In this paper, a search procedure was developed to find good short binary (N,N - 1) convolutional codes using simple rules to discard from the complete ensemble of codes a large fraction whose free distance d{free} either cannot achieve the maximum value or is equal to d_{free} of some code in the remaining set.
Journal ArticleDOI

Correction of errors in multilevel Gray coded data

TL;DR: Certain classes of low-rate binary codes that have simple decoding algorithms can be used as underlying codes in the construction of high-rate easily decodable i -compressed codes, which have higher rates than binary codes of comparable length and number of correctable errors.