Neural network error correcting decoders for block and convolutional codes

Caid, William R.; Means, R.W.

doi:10.1109/glocom.1990.116658

Cited by 21 publications

(12 citation statements)

References 3 publications

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“…First, channel decoding algorithms focus on bitlevel processing; therefore, bits, or the log-likelihood ratios (LLRs) of codewords, are conveniently treated as the inputs and expected outputs of NNs. Mainly in previous studies, the input and output nodes directly represent bits in codewords [38], or use one-hot representation (i.e., each node represents one of all possible codewords) [39] such that the corresponding vector has only one element equal to 1 and other elements equal to 0. Second, unlike the difficulty in obtaining datasets in other scenarios, man-made codewords can generate sufficient training samples and achieve labeled outputs simultaneously.…”

Section: B Channel Decodingmentioning

confidence: 99%

Deep learning for wireless physical layer: Opportunities and challenges

Wang¹,

Wen

Wang³

et al. 2017

China Commun.

532

266

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Abstract-Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

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Section: B Channel Decodingmentioning

confidence: 99%

Deep learning for wireless physical layer: Opportunities and challenges

Wang¹,

Wen

Wang³

et al. 2017

China Commun.

532

266

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“…With the advent of training techniques such as layer-by-layer unsupervised pre-training followed by gradient descent fine-tuning and back propagation, the interest for using ANNs for channel coding is renewed. Different ideas around the use of ANNs for decoding emerged in the 1990s with works such as [16]- [18] for decoding block and hamming codes. Subsequently, ANNs were used for decoding convolutional codes in [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Deep Learning based on Recurrent Neural Networks for Channel Coding

Sattiraju

Weinand

Schotten

2018

2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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Channel Coding has been one of the central disciplines driving the success stories of current generation LTE systems and beyond. In particular, turbo codes are mostly used for cellular and other applications where a reliable data transfer is required for latency-constrained communication in the presence of data-corrupting noise. However, the decoding algorithm for turbo codes is computationally intensive and thereby limiting its applicability in hand-held devices. In this paper, we study the feasibility of using Deep Learning (DL) architectures based on Recurrent Neural Networks (RNNs) for encoding and decoding of turbo codes. In this regard, we simulate and use data from various stages of the transmission chain (turbo encoder output, Additive White Gaussian Noise (AWGN) channel output, demodulator output) to train our proposed RNN architecture and compare its performance to the conventional turbo encoder/decoder algorithms. Simulation results show, that the proposed RNN model outperforms the decoding performance of a conventional turbo decoder at low Signal to Noise Ratio (SNR) regions.

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“…The Viterbi algorithm (VA) is an efficient method for decoding the convolutional code using the maximum likelihood sequence estimator technique. However, complexity, delay and memory requirement associated with the VA are relatively high and hence a sub‐optimal decoding of the convolutional code using the artificial neural network (ANN) has been intensively studied . ANN, as an alternative to the VA, has been suggested because of the low complexity, parallel processing, adaptability and fault tolerance capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…ANN, as an alternative to the VA, has been suggested because of the low complexity, parallel processing, adaptability and fault tolerance capabilities. Although the performance of ANN as an error control decoder of block codes is comparable to the optimal decoder, the performance of the ANN as a convolutional decoder is far from being satisfactory . A number of structures and approaches have been proposed to improve the performance of ANN as a convolutional decoder.…”

Section: Introductionmentioning

confidence: 99%

“…This approach makes the decoding more flexible and adaptive. Similar to previous works reported on ANN‐based decoders , we have also adopted simulation approach in this study. The soft decoding of the convolutional code using the VA offers a significant performance improvement compared with the hard decoding scheme but at the cost of increased complexity .…”

Section: Introductionmentioning

confidence: 99%

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Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network

Rajbhandari

Ghassemlooy

Angelova

2012

Trans. Emerging Tel. Tech.

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A Viterbi algorithm (VA) is the optimal decoding strategy for the convolutional code. The Viterbi algorithm is complex and requires a large memory and delay. In this paper, an alternative sub‐optimal decoder based on the artificial neural network (ANN) is proposed and studied using a sliding block decoding algorithm. The ANN is trained in a supervised manner and the system parameters are optimised using computer simulations for the optimum performance. Comparative study with the Viterbi decoder is carried out. The performance of the ANN decoder is found to be comparable to the Viterbi ‘soft’ decoding with much reduced decoding length. The key advantages of the proposed ANN decoder compared with other ANN decoders are the reduced decoding and training length, adaptive decoding, no iteration required and possibility of parallel decoding. Copyright © 2012 John Wiley & Sons, Ltd.

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Neural network error correcting decoders for block and convolutional codes

Cited by 21 publications

References 3 publications

Deep learning for wireless physical layer: Opportunities and challenges

Deep learning for wireless physical layer: Opportunities and challenges

Performance Analysis of Deep Learning based on Recurrent Neural Networks for Channel Coding

Adaptive ‘soft’ sliding block decoding of convolutional code using the artificial neural network

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