DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes

Cui, Jianming; Kong, Wenxiu; Zhang, Xiaojun; Chen, Da; Zeng, Qingtian

doi:10.3390/e23070863

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…This paper attempts to introduce double-layer Long Short-Term Memory (DLSTM) [51], CNN-LSTM [52], and Bidirectional LSTM (BiLSTM) [53] deep learning models for bathymetry inversion using satellite multispectral and hyperspectral images. Considering that BiLSTM can store bidirectional coding information at the same time, and aiming at the importance of the spectral features of satellite images, a BoBiLSTM (Bandoptimized BiLSTM) model is proposed to improve the performance of bathymetry inversion.…”

Section: Introductionmentioning

confidence: 99%

Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

Chen

Wang

et al. 2023

Remote Sensing

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Shallow water bathymetry is of great significance in understanding, managing, and protecting coastal ecological environments. Many studies have shown that both empirical models and deep learning models can achieve promising results from satellite imagery bathymetry inversion. However, the spectral information available today in multispectral or/and hyperspectral satellite images has not been explored thoroughly in many models. The Band-optimized Bidirectional Long Short-Term Memory (BoBiLSTM) model proposed in this paper feeds only the optimized bands and band ratios to the deep learning model, and a series of experiments were conducted in the shallow waters of Molokai Island, Hawaii, using hyperspectral satellite imagery (PRISMA) and multispectral satellite imagery (Sentinel-2) with ICESat-2 data and multibeam scan data as training data, respectively. The experimental results of the BoBiLSTM model demonstrate its robustness over other compared models. For example, using PRISMA data as the source image, the BoBiLSTM model achieves RMSE values of 0.82 m (using ICESat-2 as the training data) and 1.43 m (using multibeam as the training data), respectively, and because of using the bidirectional strategy, the inverted bathymetry reaches as far as a depth of 25 m. More importantly, the BoBiLSTM model does not overfit the data in general, which is one of its advantages over many other deep learning models. Unlike other deep learning models, which require a large amount of training data and all available bands as the inputs, the BoBiLSTM model can perform very well using equivalently less training data and a handful of bands and band ratios. With ICESat-2 data becoming commonly available and covering many shallow water regions around the world, the proposed BoBiLSTM model holds potential for bathymetry inversion for any region around the world where satellite images and ICESat-2 data are available.

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Section: Introductionmentioning

confidence: 99%

Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

Chen

Wang

et al. 2023

Remote Sensing

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“…To improve the SNR of received symbols, a denoiser based on residual learning was introduced before the neural network decoder (NND) in [ 29 ], which was characterized as a residual neural network decoder (RNND). In [ 30 ], a double long short-term memory (DLSTM) neural network was proposed to improve the performance of short block length by clipping frozen bits to enhance prediction accuracy. Deep learning-based decoders perform better than traditional decoders [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement

Song

Feng

Wang

2022

Entropy

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Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the deep neural network is treated as a black box and learns to map the polar codes with noise to the original information code directly. In fact, it is difficult for the network to distinguish between valid and interfering information, which leads to limited BER performance. In this paper, a deep residual network based on information refinement (DIR-NET) is proposed for decoding polar-coded short packets. The proposed method works to fully distinguish the effective and interference information in the codewords, thus obtaining a lower bit error rate. To achieve this goal, we design a two-stage decoding network, including a denoising subnetwork and decoding subnetwork. This structure can further improve the accuracy of the decoding method. Furthermore, we construct the whole network solely on the basis of the attention mechanism. It has a stronger information extraction ability than the traditional neural network structure. Benefiting from cascaded attention modules, information can be filtered and refined step-by-step, thus obtaining a low bit error rate. The simulation results show that DIR-Net outperforms existing decoding methods in terms of BER performance under both AWGN channels and flat fading channels.

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Deep Learning-Assisted Adaptive Dynamic-SCLF Decoding of Polar Codes

Li,

Zhou,

et al. 2024

IEEE Trans. Cogn. Commun. Netw.

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DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes

Cited by 3 publications

References 21 publications

Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement

Deep Learning-Assisted Adaptive Dynamic-SCLF Decoding of Polar Codes

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