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5th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Sustainable Energy Information Technology (SEIT)

作者： Amani, Elie Djouani, Karim Kurien, Anish Tshwane Univ Technol FSATI Dept Elect Engn ZA-0001 Pretoria South Africa UPEC LISSI Lab 12 Creteil France

The 4x4 perfect space-time block code (STBC) is one type in a family of perfect STBCs that have full rate, full diversity, a non-vanishing constant minimum determinant that improves spectral efficiency, uniform average transmitted energy per antenna, and good shaping. These codes suffer very high complexity maximum likelihood (ML) decoding. The exhaustive ML decoding of the 4x4 perfect STBC for instance has complexity of O(N-16), with N being the size of the underlying QAM constellation. This paper suggests a fast decoding algorithm for the 4x4 perfect STBC that reduces decoding complexity from O(N-16) to O(N-8). The algorithm is based on conditional minimization of the ML decision metric with respect to one subset of symbols given another. This low complexity decoding approach is essentially ML because it suffers no loss in symbol error rate (SER) performance when compared to the exhaustive ML decoding. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： low complexity decoding maximum likelihood decoding multiple-input multiple-output (MIMO) perfect space-time block code

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low complexity decoding for Spinal Codes: Sliding Feedback decoding 86

Low Complexity Decoding for Spinal Codes: Sliding Feedback D...

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86th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Xu, Siyue Wu, Shaohua Luo, Junxin Jiao, Jian Zhang, Qinyu Harbin Inst Technol Shenzhen Grad Sch Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781509059355

As a type of newly invented rateless codes, Spinal codes are characterized by capacity achieving over both additive white Gaussian noise (AWGN) and binary symmetric channel (BSC) with short message length and pseudo-random like code-words. For the emerging ultra-reliable low-latency communication (URLLC) scenarios such as information exchanging between self-drive cars, Spinal codes hold great prospects. However, the high decoding complexity of Spinal codes remains a bottleneck for its practical applications. In this work, a novel low complexity decoding algorithm named sliding feedback decoding (SFD) for Spinal codes is proposed. By 'sliding', the decoding tree is layered by a sliding window. By 'feedback decoding', the optimal parent node decision for each layer located by the sliding window is made by the feedback from the best leaf node in the located layer. And the final decoding path is composed of all the optimal parent nodes selected layer by layer. The complexity of the proposed algorithm is analyzed theoretically, and the results show that it is lower than the complexity of other algorithm. Extensive simulations are carried out to verify the effectiveness of the proposed algorithm. Compared with the bubble decoder and the forward stack decoding (FSD) proposed in the literature, SFD can significantly reduce the decoding complexity without any harming to the rate performance.

关键词： Spinal codes low complexity decoding sliding feedback decoding

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A new high rate code scheme with highly parallel and low complexity decoding algorithm

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IEEE COMMUNICATIONS LETTERS 2004年第6期8卷 386-387页

作者： Liao, Y Kieffer, JC Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

A new high rate code scheme is proposed in this paper. It consists of serial concatenated recursive systematic ordinary (nonpunctured) convolutional codes with only 8 states in the trellis of the corresponding reciprocal dual codes. With a low complexity and highly parallel decoding algorithm, over additive white Gaussian noise channels, the proposed codes can achieve good bit error rate (BER) performance comparable to that of turbo codes and low density parity check (LDPC) codes. At code rate R = 16/17, the overall decoding complexity of the proposed code scheme is almost half that of the LDPC codes.

关键词： high rate codes low complexity decoding

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A low complexity decoding Algorithm for Majority-Logic Decodable Nonbinary LDPC Codes

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IEEE COMMUNICATIONS LETTERS 2010年第11期14卷 1062-1064页

作者： Zhao, Dayuan Ma, Xiao Chen, Chao Bai, Baoming Sun Yat Sen Univ Dept Elect & Commun Engn Guangzhou 510275 Guangdong Peoples R China Xidian Univ State Key Lab ISN Xian Peoples R China

In this letter, we propose a low complexity decoding algorithm for majority-logic decodable nonbinary low-density parity-check (LDPC) codes. The proposed algorithm is initialized with the quantized squared Euclidean distances between the constellation points and the received signals. Like most of the existing reliability-based decoding algorithms, the proposed algorithm requires only integer operations and finite field operations and (hence) can be implemented with simple combinational logic circuits in practical systems. Simulation results show that the proposed algorithm suffers from a little performance degradation compared with FFT-QSPA. The algorithm provides a candidate for trade-offs between performance and complexity.

关键词： low complexity decoding majority-logic decodable nonbinary LDPC codes FFT-QSPA

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low complexity decoding of the 4×4 Perfect Space-time Block Code

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Procedia Computer Science 2014年 32卷 223-228页

作者： Elie Amani Karim Djouani Anish Kurien FSATI/Dept. of Electrical Engineering Tshwane University of Technology (TUT) Pretoria 0001 South Africa Université Paris-Est Créteil (UPEC) 12/LISSI Lab Créteil France

The 4x4 perfect space-time block code (STBC) is one type in a family of perfect STBCs that have full rate, full diversity, a non- vanishing constant minimum determinant that improves spectral efficiency, uniform average transmitted energy per antenna, and good shaping. These codes suffer very high complexity maximum likelihood (ML) decoding. The exhaustive ML decoding of the 4x4 perfect STBC for instance has complexity of O(N 16 ) , with N being the size of the underlying QAM constellation. This paper suggests a fast decoding algorithm for the 4x4 perfect STBC that reduces decoding complexity from O(N 16 ) to O(N 8 ) . The algorithm is based on conditional minimization of the ML decision metric with respect to one subset of symbols given another. This low complexity decoding approach is essentially ML because it suffers no loss in symbol error rate (SER) performance when compared to the exhaustive ML decoding.

关键词： low complexity decoding maximum likelihood decoding multiple-input multiple-output (MIMO) perfect space-time block code.

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On the Design of low complexity decoding (LCD) Network Codes

On the Design of Low Complexity Decoding (LCD) Network Codes

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IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS)

作者： Hu, D. Y. Wang, M. Z. Lau, F. C. M. Peng, Q. C. Hong Kong Polytech Univ Elect & Informat Engn Dept Hong Kong Hong Kong Peoples R China Univ Elect & Sci Technol China Sch Commun & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9781424458516

Network coding has recently attracted significant attention as its new paradigm promises to benefit various areas of communication and networks. The benefits come at a cost since encoding operations are required at intermediate nodes, and decoding operations are required at destination nodes. In some applications, the receive ends (the destinations) cannot, or may not want to, perform complex decoding operations. Hence, it is desirable to design network codes that can be decoded with a minimal complexity. We propose a design algorithm that results in network codes with a lower decoding complexity. This is achieved by minimizing the number of operations at the sinks to recover data sent by the source. Ideally, the sinks just receive "native" packets (packets that are in their original form) and hence only require perhaps some "shuffling" (permutation) operations. To achieve maximum throughput, in general, the ideal case does not happen too often. Hence there is a need to find low complexity decoding (LCD) network codes. We make use of a concept known as information decomposition to classify different information flows on all the links in a network. A greedy search algorithm examines the information flows on the links to the sinks and assigns global coding vectors in such a way that the resultant network code is a LCD network code, in other words, fewer operations, compared to other network codes, are required to recover data transmitted by the source. We define a complexity measure for the decoding operations and investigate the performance of LCD codes and compare the decoding complexity of LCD codes with that of network codes obtained by the conventional polynomial time algorithm. We demonstrate, by using 500 random networks with 50 nodes, that the average decoding complexity of our network codes is about 20 folds lower than that of the conventional network codes. The experiment also demonstrates that the number of "native" packets arriving at the sinks in our LCD

关键词： Network coding low complexity decoding information decomposition greedy

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low complexity Unquantized Forward Stack decoding Algorithm for Spinal Codes in Measurement While Drilling Communication

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IEEE ACCESS 2025年 13卷 22637-22646页

作者： Yu, Xiaoyang Liang, Lei Jiang, Ke Chen, Tianwei Wuhan Univ Technol Natl Engn Res Ctr Fiber Opt Sensing Technol & Networks Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Wuhan Univ Technol Sch Safety Sci & Emergency Management Wuhan 430070 Peoples R China

The complex environments of Measurement-While-Drilling (MWD), characterized by intense vibrations and high temperatures, can lead to random attenuation of optical intensity, phase mismatch, and signal distortion, thereby reducing the signal-to-noise ratio (SNR) of optical signals. Under severe channel fluctuations, forward stack decoding (FSD) tends to search more invalid nodes, increasing decoding complexity. This paper proposes an unquantized forward stack decoding algorithm that dynamically adjusts the number of retained candidate nodes based on channel conditions. With the ability to evaluate channel status dynamically, the proposed method achieves low-complexity decoding and enhances transmission performance. In simulations under channel conditions with SNR ranging from -14 to 5 dB, the average decoding complexity was reduced by 58% compared to conventional FSD. In communication experiments using a dynamically variable optical attenuator to generate random attenuation between 0 and -30 dB, the average decoding complexity was reduced by 23.6% compared to FSD. Furthermore, this work integrates the non-quantized forward stack decoding with the superposition UEP-Spinal code to construct a novel MWD communication system. In simulated MWD environment, the proposed system achieved an average bit error rate (BER) of $5.58\times 10<^>{-5}$ , with BER consistently below 0.1% across all intervals. Compared to the traditional Spinal code system, the decoding complexity was reduced by 31.5%. The results demonstrate that the newly developed communication system exhibits high reliability, low decoding latency, and high effective transmission rates in simulated drilling platform scenarios, making it well-suited for MWD communication applications.

关键词： Rateless codes spinal code spinal code sequential decoding sequential decoding low complexity decoding low complexity decoding low complexity decoding

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low complexity Bit Reliability and Predication Based Symbol Value Selection decoding Algorithms for Non-Binary LDPC Codes

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IEEE ACCESS 2020年 8卷 142691-142703页

作者： Ullah, Waheed Cheng, Ling Takawira, Fambirai Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa

The main challenge for hardware implementation of non-binary LDPC decoding is the high computational complexity and large memory requirement. To address this challenge, five new low complexity LDPC decoding algorithms are proposed in this paper. The proposed algorithms are developed specifically towards the low complexity, yet effective, decoding of the NB LDPC codes. The proposed decoding algorithms update, iteratively, the hard decision received vector to search for the valid codeword in the vector space of Galois field (GF). The selection criterion for least reliable symbol positions is based on the information from the failed checks and the reliability information from the Galois field structure as well as from the received channel soft information. To choose the correct value for the candidate symbol, two methods are used. The first method is based on the prediction of the error symbol from the set of Galois field symbols which maximize an objective function. In the second method, individual bits are flipped based on the reliability information obtained from the channel. Algorithms 1 and 2 flip a single symbol per iteration whilst the other three algorithms 3, 4 and 5 flip multiple symbols in each iteration. The proposed voting based Algorithms 1, 2 and 5 first short list the unreliable positions using a majority voting scheme and then choose the candidate symbol value from the set of the symbols in GF(q) while not violating the field order q. These methods simplify the decoding complexity in terms of computation and memory. Results and analysis of these algorithms show an appealing tradeoff between computational complexity and bit error rate performance for NB LDPC codes.

关键词： Multiple vote non-binary LDPC iterative reliability decoding symbol flipping sum product algorithm low complexity decoding

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low complexity Non-binary Turbo decoding based on the Local-SOVA Algorithm 11

Low Complexity Non-binary Turbo Decoding based on the Local-...

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11th International Symposium on Topics in Coding (ISTC)

作者： Le Blevec, Hugo Klaimi, Rami Weithoffer, Stefan Nour, Charbel Abdel Baghdadi, Amer IMT Atlantique Lab STICC UMR CNRS 6285 F-29238 Brest France

ISBN: (纸本)9781665409438

Non-binary Turbo codes have been shown to out-perform their binary counterparts in terms of error correcting performance yet the decoding complexity of the commonly used MM-Log-MAP algorithm prohibits efficient hardware implementations. In this work, we apply for the first time the recently proposed Local SOYA algorithm for decoding non-binary Turbo codes. Moreover, we propose a low complexity variant dedicated to the direct association with high order constellations denoted by the nearest neighbor Local SOYA. It considers only a limited amount of nearest competing constellation symbols for the soft output computation. Simulation results show that this approach allows a complexity reduction of up to 52% in terms of addcompare-select operations while maintaining the same error correcting performance compared to the MM-Log-MAP algorithm. It can even reach up to 80% if high code rates or frame error rates higher than 10(-4) are targeted. The achieved complexity reduction represents a significant step forward towards hardware implementation.

关键词： Non-binary Turbo codes low complexity decoding Local SOYA

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Baseline parity-check matrix for iterative soft-decision decoding of binary cyclic codes

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IET COMMUNICATIONS 2020年第12期14卷 1968-1974页

作者： Babalola, Oluwaseyi P. Ogundile, Olayinka O. Versfeld, Daniel Jaco J. Stellenbosch Univ Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa Tai Solarin Univ Educ Dept Comp Sci Ijebu 2118 Nigeria

Belief propagation (BP) is a soft-decision (SD) decoder that is commonly used to obtain near-optimal decoding performance for linear codes defined over sparse parity-check matrix. Nevertheless, the BP yields poor performance when used in the decoding of algebraic block codes, usually described by a dense parity-check matrix. Hence, enhanced BP decoders transform the parity check matrix of such codes at each iteration for efficient decoding. In this article, the performance of the transformed parity-check matrix of the iterative SD decoder is analysed for the class of binary cyclic codes using a perfect knowledge model (PKM). The PKM computes a list of candidate matrices and selects a baseline parity-check matrix according to a distance metric. The selected matrix is optimal since it minimizes the probability of error over various choices in the list. Results show that, for a given channel condition, the conventional transformed matrix obtained by Gaussian elimination is sub-optimal and does not necessarily contain unitary weighted columns at corresponding columns of the unreliable bits. Moreover, PKM can be used to verify the performances of newly developed iterative SD decoders for binary cyclic codes based on parity-check equations instead of maximum-likelihood decoding.

关键词： parity check codes maximum likelihood decoding matrix algebra decoding cyclic codes iterative decoding binary codes Gaussian processes error statistics binary cyclic codes low complexity decoding linear code maximum-likelihood decoding soft-decision decoding algorithm near-optimal decoding performance sparse parity-check matrix BP yields poor performance algebraic block codes iterative soft-decision decoding iterative SD decoder BP decoding algorithms ML decoding baseline parity-check matrix transformation algorithm belief propagation PKM perfect knowledge model error correction Gaussian elimination error probability

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