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Author

Hangxuan Cui

Bio: Hangxuan Cui is an academic researcher from Nanjing University. The author has contributed to research in topics: Decoding methods & Low-density parity-check code. The author has an hindex of 3, co-authored 13 publications receiving 24 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel hard-decision decoding algorithm, called TRGDBF, which can achieve up to two orders of magnitude better error-correction performance than the probabilistic gradient descent bit-flipping (PGDBF) algorithm and reduce the average iteration count by about 15%.
Abstract: Low-complexity and high-performance low-density parity-check (LDPC) decoders are highly demanded in various modern communication and storage systems. In this paper, a novel hard-decision decoding algorithm, called tabu-list random-penalty gradient descent bit-flipping (TRGDBF) algorithm, is proposed. Compared to the state-of-the-art hard-decision algorithms, the TRGDBF algorithm has much better error-correction performance due to several algorithmic improvements. First, a random-penalty term is introduced to the inversion function to help the decoder escape from trapping sets, which are the main causes of the error-floor phenomenon. Second, a tabu-list is employed to improve the decoding efficiency. Numerical results show that the TRGDBF algorithm can achieve up to two orders of magnitude better error-correction performance than the probabilistic gradient descent bit-flipping (PGDBF) algorithm and reduce the average iteration count by about 15%. In addition, a well-optimized hardware architecture is developed to implement the TRGDBF algorithm. Algorithmic transformation and architecture optimization are well explored to reduce the hardware complexity and latency. Synthesis results show that the TRGDBF decoder can work at a higher frequency and offer a larger throughput than the PGDBF decoder.

23 citations

Journal ArticleDOI
TL;DR: The problem is solved gracefully by developing a low-complexity check-node update function, greatly improving the reliability of check-to-variable messages and an efficient 5G LDPC decoder architecture is presented.
Abstract: Low-density parity-check (LDPC) code as a very promising error-correction code has been adopted as the channel coding scheme in the fifth-generation (5G) new radio. However, it is very challenging to design a high-performance decoder for 5G LDPC codes because their inherent numerous degree-1 variable-nodes are very prone to be erroneous. In this article, the problem is solved gracefully by developing a low-complexity check-node update function, greatly improving the reliability of check-to-variable messages. By further incorporating the proposed column degree adaptation strategy, our decoder could offer a 0.4dB performance gain over the existing ones. In addition, this article presents an efficient 5G LDPC decoder architecture. Benefiting the specific structure of 5G LDPC codes, layer merging, split storage method, and selective-shift structure are introduced to facilitate a significant reduction of decoding delay and area consumption. Implementation result on 90-nm CMOS technology demonstrates that the proposed decoder architecture yields an impressive improvement in throughput-to-area ratio, achieving up to 173.3% compared to conventional design.

15 citations

Journal ArticleDOI
TL;DR: In the ISBF decoder, the global maximum-finding operation can be executed in parallel to other decoding operations, significantly shortening the critical path and a nonuniform flipping rule is incorporated to achieve a better decoding performance.
Abstract: Tabu-list random-penalty gradient descent bit-flipping (TRGDBF) decoder is the state-of-the-art hard-decision low-density parity-check (LDPC) decoder in terms of error-correction performance on binary symmetric channel (BSC). However, the TRGDBF decoder suffers from a long critical path caused by the global maximum-finding operation, limiting the achievable throughput. This brief proposes an information storage bit-flipping (ISBF) decoder to solve this problem. Different from the existing bit-flipping (BF) decoders which adopt serial decoding manner, in the ISBF decoder, by storing the previous decoding information, the global maximum-finding operation can be executed in parallel to other decoding operations, significantly shortening the critical path. Moreover, a nonuniform flipping rule is incorporated to achieve a better decoding performance. We also present an efficient architecture to implement the ISBF decoder. The design example demonstrates that compared to other hard-decision BF decoders, the ISBF decoder could provide both the best decoding performance and throughput on BSC.

12 citations

Journal ArticleDOI
TL;DR: A novel class of hard-decision algorithms for decoding low density parity check codes, named fine-grained bit-flipping (FBF) algorithms, employ a detailed classification of each bit, by introducing the XOR value of its estimated and received value as a subdividing criterion.
Abstract: This brief presents a novel class of hard-decision algorithms for decoding low density parity check codes. The new algorithms, named fine-grained bit-flipping (FBF) algorithms, employ a detailed classification of each bit, by introducing the XOR value of its estimated and received value as a subdividing criterion. The fine-grained classification allows the algorithms to strengthen the information utilization during each iteration. Simulation results show that the FBF algorithms can achieve up to 5 times better decoding performance than the state-of-the-art bit-flipping algorithms over the binary symmetric channel. Additionally, a well-optimized hardware architecture is developed for implementing FBF algorithms. Compared to other decoders, implementation results demonstrate that the FBF decoders achieve higher throughput and area efficiency.

9 citations

Proceedings ArticleDOI
26 May 2019
TL;DR: A tabu-list aided PGDBF (T-PGDBF) algorithm, employed to help the decoding escape from trapping sets, which is the main cause of the error-floor phenomenon, which can reach that of soft-decision algorithms.
Abstract: Probabilistic gradient descent bit-flipping (PGDBF) is the state-of-the-art hard-decision algorithm for decoding low-density parity-check (LDPC) codes on binary symmetric channel (BSC). However, there still exists a considerable performance gap between the PGDBF algorithm and soft-decision algorithms, especially in the error-floor region. To bridge this performance gap, a tabu-list aided PGDBF (T-PGDBF) algorithm is proposed in this paper. In the T-PGDBF algorithm, a tabu-list is employed to help the decoding escape from trapping sets, which is the main cause of the error-floor phenomenon. The bits which are flipped in the current iteration will be added to the tabu-list to prevent them being flipped in the next iteration. Simulation results show that the T-PGDBF algorithm offers a significant performance gain when compared to the PGDBF algorithm, which can reach that of soft-decision algorithms. We also present the hardware architecture to implement the T-PGDBF algorithm. Synthesis results show that the improved performance offered by the T-PGDBF algorithm can be obtained with a small hardware overhead.

5 citations


Cited by
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Book ChapterDOI
01 Feb 2013

79 citations

Posted Content
TL;DR: In this paper, the authors provide a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks.
Abstract: The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.

69 citations

Journal ArticleDOI
TL;DR: In this paper , a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks is presented.
Abstract: The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of Things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in the fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. In particular, the potential of using emerging deep learning and federated learning techniques for enhancing the efficiency and security of IoT communication are discussed, and their promises and challenges are introduced. Finally, future research directions toward beyond 5G IoT networks are pointed out.

68 citations

Journal ArticleDOI
TL;DR: A novel way to reduce blockchain nodes’ memory requirements using error correcting codes, which encodes data across multiple blocks, respectively, block headers, in the blockchain, and applies to blockchains organized in two different ways.
Abstract: This article presents a novel way to reduce blockchain nodes’ memory requirements using error correcting codes. In particular, LDPC codes are taken as examples to explicitly demonstrate the scheme. The proposed coding scheme encodes data across multiple blocks, respectively, block headers, in the blockchain. This leads to a significant reduction in required memory at each node. We then apply the proposed coding technique to blockchains organized in two different ways. Our first scheme has the same protocol for mining, broadcasting, and verification of blocks, as Bitcoin-type blockchains. Our scheme is different in that full nodes do not have to store all blocks. Instead they will need to store only one block of a group of $t$ blocks. In the second scheme, we consider a new block verification protocol and an account-based model under the assumption that transmission between any two nodes can be established, as well as the broadcast transmission. Our block verification protocol uses the Byzantine fault tolerance algorithm and requires sending a newly mined block to only a small number of verification nodes, instead of broadcasting it to the entire network, which leads to a reduction of the network load.

25 citations

Journal ArticleDOI
TL;DR: A novel hard-decision decoding algorithm, called TRGDBF, which can achieve up to two orders of magnitude better error-correction performance than the probabilistic gradient descent bit-flipping (PGDBF) algorithm and reduce the average iteration count by about 15%.
Abstract: Low-complexity and high-performance low-density parity-check (LDPC) decoders are highly demanded in various modern communication and storage systems. In this paper, a novel hard-decision decoding algorithm, called tabu-list random-penalty gradient descent bit-flipping (TRGDBF) algorithm, is proposed. Compared to the state-of-the-art hard-decision algorithms, the TRGDBF algorithm has much better error-correction performance due to several algorithmic improvements. First, a random-penalty term is introduced to the inversion function to help the decoder escape from trapping sets, which are the main causes of the error-floor phenomenon. Second, a tabu-list is employed to improve the decoding efficiency. Numerical results show that the TRGDBF algorithm can achieve up to two orders of magnitude better error-correction performance than the probabilistic gradient descent bit-flipping (PGDBF) algorithm and reduce the average iteration count by about 15%. In addition, a well-optimized hardware architecture is developed to implement the TRGDBF algorithm. Algorithmic transformation and architecture optimization are well explored to reduce the hardware complexity and latency. Synthesis results show that the TRGDBF decoder can work at a higher frequency and offer a larger throughput than the PGDBF decoder.

23 citations