On deep learning-based channel decoding

Gruber, Tobias; Cammerer, Sebastian; Hoydis, Jakob; Brink, Stephan ten

doi:10.1109/ciss.2017.7926071

Cited by 492 publications

(375 citation statements)

References 21 publications

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“…Besides, it is important to note that in the offline training phase, the number of stages is fixed to 50 and 70 for C 1 and C 2 codes, respectively. According to (9c), (11) and (12), the total computational complexity of the ADMM L2 decoder in each iteration is roughly O(N + Γa) real multiplications + O(10(N + Γa) − 1) real applications + 2 real divisions. Since LADN (LADN I) finally perform as the ADMM L2 decoder loaded with learned parameters {α, µ} ({α, µ}), its computational complexity is the same as the ADMM L2 decoder, which is lower than that of the ML decoder, i.e., O(2 N ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Recent advances in deep learning (DL) provide a new direction to tackle tough signal processing tasks in communication systems, such as channel estimation [9], MIMO detection [10] and channel coding [11]- [13]. For channel coding, the work [11] proposed to use a fully connected neural network and showed that the performance of the network approaches that of the ML decoder for very small block codes. Then, in [12], the authors proposed to employ the recurrent neural network The authors are with College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (email: {21731133, zmmblack, mjzhao, lm1029}@zju.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

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ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning

2020

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Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the alternating direction method of multipliers (ADMM)-penalized decoder. In addition, we propose two improved versions of the proposed network. The first one transforms the penalty parameter into a set of iteration-dependent ones, and the second one adopts a specially designed penalty function, which is based on a piecewise linear function with adjustable slopes. Numerical results show that the resulting DL-aided decoders outperform the original ADMM-penalized decoder for various low density parity check (LDPC) codes with similar computational complexity.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning

2020

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“…Recently, the deep learning (DL) techniques have been developed rapidly and they have shown superior performance in many aspects of communication systems [17,18]. In this paper, we propose a novel DL-aided approach to design the read thresholds dynamically for the MLC flash memories.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Aided Dynamic Read Thresholds Design for Multi-Level-Cell Flash Memories

Mei

Cai

2020

IEEE Trans. Commun.

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The practical NAND flash memory suffers from various non-stationary noises that are difficult to be predicted. Furthermore, the data retention noise induced channel offset is unknown during the readback process. This severely affects the data recovery from the memory cell. In this paper, we first propose a novel recurrent neural network (RNN)-based detector to effectively detect the data symbols stored in the multi-levelcell (MLC) flash memory without any prior knowledge of the channel. However, compared with the conventional threshold detector, the proposed RNN detector introduces much longer read latency and more power consumption. To tackle this problem, we further propose an RNN-aided (RNNA) dynamic threshold detector, whose detection thresholds can be derived based on the outputs of the RNN detector. We thus only need to activate the RNN detector periodically when the system is idle. Moreover, to enable soft-decision decoding of error-correction codes, we first show how to obtain more read thresholds based on the hard-decision read thresholds derived from the RNN detector. We then propose integer-based reliability mappings based on the designed read thresholds, which can generate the soft information of the channel. Finally, we propose to apply density evolution (DE) combined with differential evolution algorithm to optimize the read thresholds for LDPC coded flash memory channels. Computer simulation results demonstrate the effectiveness of our RNNA dynamic read thresholds design, for both the uncoded and LDPC-coded flash memory channels, without any prior knowledge of the channel.

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confidence: 99%

“…Recently several works have reported the application of DNNs to the decoding of error control codes (ECCs) [28]- [33]. A DNN enables low-latency decoding since it enables one-shot decoding, where the DNN finds its estimate by passing each layer only once [28], [31], [32]. In addition, DNNs can efficiently execute in parallel and be implemented with low-precision data types on a graphical processing unit (GPU), field programmable gate array (FPGA), or application specific integrated circuit (ASIC) [28], [31]- [33], [35].…”

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confidence: 99%

Deep Learning-Based Decoding for Constrained Sequence Codes

Cao

Fair

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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Constrained sequence (CS) codes, including fixedlength CS codes and variable-length CS codes, have been widely used in modern wireless communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel to enable efficient and reliable transmission of coded symbols. In this paper, we propose using deep learning approaches to decode fixed-length and variable-length CS codes. Traditional encoding and decoding of fixed-length CS codes rely on look-up tables (LUTs), which is prone to errors that occur during transmission. We introduce fixed-length constrained sequence decoding based on multiple layer perception (MLP) networks and convolutional neural networks (CNNs), and demonstrate that we are able to achieve low bit error rates that are close to maximum a posteriori probability (MAP) decoding as well as improve the system throughput. Further, implementation of capacity-achieving fixedlength codes, where the complexity is prohibitively high with LUT decoding, becomes practical with deep learning-based decoding. We then consider CNN-aided decoding of variable-length CS codes. Different from conventional decoding where the received sequence is processed bit-by-bit, we propose using CNNs to perform one-shot batch-processing of variable-length CS codes such that an entire batch is decoded at once, which improves the system throughput. Moreover, since the CNNs can exploit global information with batch-processing instead of only making use of local information as in conventional bit-by-bit processing, the error rates can be reduced. We present simulation results that show excellent performance with both fixed-length and variable-length CS codes that are used in the frontiers of wireless communication systems.

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On deep learning-based channel decoding

Cited by 492 publications

References 21 publications

ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning

ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning

Deep Learning-Aided Dynamic Read Thresholds Design for Multi-Level-Cell Flash Memories

Deep Learning-Based Decoding for Constrained Sequence Codes

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