Sequential decoding

About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.

A Simple, Fast Diverse Decoding Algorithm for Neural Generation.

Abstract: In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an inter-sibling ranking penalty, favoring choosing hypotheses from diverse parents. We evaluate the proposed model on the tasks of dialogue response generation, abstractive summarization and machine translation. We find that diverse decoding helps across all tasks, especially those for which reranking is needed. We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL). We observe a further performance boost from this RL technique. This paper includes material from the unpublished script "Mutual Information and Diverse Decoding Improve Neural Machine Translation" (Li and Jurafsky, 2016).

Trellis-coded modulation with bit interleaving and iterative decoding

Abstract: This paper considers bit-interleaved coded modulation (BICM) for bandwidth-efficient transmission using software radios. A simple iterative decoding (ID) method with hard-decision feedback is suggested to achieve better performance. The paper shows that convolutional codes with good Hamming-distance property can provide both high diversity order and large free Euclidean distance for BICM-ID. The method offers a common framework for coded modulation over channels with a variety of fading statistics. In addition, BICM-ID allows an efficient combination of punctured convolutional codes and multiphase/level modulation, and therefore provides a simple mechanism for variable-rate transmission.

An Iterative BP-CNN Architecture for Channel Decoding

Abstract: Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation – convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and, hence, result in better decoding performance. To train a well-behaved CNN model, we define a new loss function that involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the bit error rate of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. 1 1 Code is available at https://github.com/liangfei-info/Iterative-BP-CNN .

Decoding algebraic-geometric codes up to the designed minimum distance

Abstract: A simple decoding procedure for algebraic-geometric codes C/sub Omega /(D,G) is presented. This decoding procedure is a generalization of Peterson's decoding procedure for the BCH codes. It can be used to correct any ((d*-1)/2) or fewer errors with complexity O(n/sup 3/), where d* is the designed minimum distance of the algebraic-geometric code and n is the codelength. >

Hardware architectures for successive cancellation decoding of polar codes

Abstract: The recently-discovered polar codes are widely seen as a major breakthrough in coding theory. These codes achieve the capacity of many important channels under successive cancellation decoding. Motivated by the rapid progress in the theory of polar codes, we propose a family of architectures for efficient hardware implementation of successive cancellation decoders. We show that such decoders can be implemented with O(n) processing elements and O(n) memory elements, while providing constant throughput. We also propose a technique for overlapping the decoding of several consecutive codewords, thereby achieving a significant speed-up factor. We furthermore show that successive cancellation decoding can be implemented in the logarithmic domain, thereby eliminating the multiplication and division operations and greatly reducing the complexity of each processing element.