Journal articles: 'Decoding performance'

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Ravi Kumar, Ch, and K. Padmaraju. "Hard Decision Decoding Performance Improved Using Turbo Product Codes." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 228. http://dx.doi.org/10.14419/ijet.v7i3.12.16030.

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The performance of soft decision decoding, whose for which the design is complex, is superior to the performance of hard decision decoding. In this paper, we propose a turbo product code with a bit flip algorithm to improve the performance of hard decision decoding. The performance of hard decision decoding is improved with low complexity using multidimensional turbo product codes. The reliability of decoding in a communication system to detect and correct errors is discussed .Maximum a posterior probability (MAP) decoding is employed to improve the hard decision performance of turbo product codes with multiple dimensions. Our results include comparisons of multiple dimensions—2D, 3D, 4D, and 5D—and the number of iterations in soft and hard decision decoding.

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Liu, Hengyan, Limin Zhang, Wenjun Yan, and Qing Ling. "Neural-Network-Assisted Polar Code Decoding Schemes." Applied Sciences 12, no. 24 (December 11, 2022): 12700. http://dx.doi.org/10.3390/app122412700.

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The traditional fast successive-cancellation (SC) decoding algorithm can effectively reduce the decoding steps, but the decoding adopts a sub-optimal algorithm, so it cannot improve the bit error performance. In order to improve the bit error performance while maintaining low decoding steps, we introduce a neural network subcode that can achieve optimal decoding performance and combine it with the traditional fast SC decoding algorithm. While exploring how to combine neural network node (NNN) with R1, R0, single-parity checks (SPC), and Rep, we find that the decoding failed sometimes when the NNN was not the last subcode. To solve the problem, we propose two neural network-assisted decoding schemes: a key-bit-based subcode NN-assisted decoding (KSNNAD) scheme and a last subcode NN-assisted decoding (LSNNAD) scheme. The LSNNAD scheme recognizes the last subcode as an NNN, and the NNN with nearly optimal decoding performance gives rise to some performance improvements. To further improve performance, the KSNNAD scheme recognizes the subcode with a key bit as an NNN and changes the training data and label accordingly. Computer simulation results confirm that the two schemes can effectively reduce the decoding steps, and their bit error rates (BERs) are lower than those of the successive-cancellation decoder (SCD).

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Mu, Xiao Dong, Xiao Lin Niu, Shao Wang Shi, and Wei Song. "GPU-Based High Performance Terrain Compression." Applied Mechanics and Materials 321-324 (June 2013): 1234–37. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1234.

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It has been well accepted that compression is essential to the real-time visualization of large-terrain elevation data. To achieve efficient performance, a terrain compression method featuring high decoding speed is proposed in this paper. A combined prediction scheme is applied in the prediction stage. Golomb-Rice coding is then used to encode the residuals after the prediction stage. The style of the codeword is tailored to make the decoding process amenable to a GPU implementation. Then batching is used to further improve the decoding speed. The proposed method is easy to implement and simple to integrate into existing systems. Experiments show that the compression rate is better than PNG. In terms of decompression efficiency, this method archives a decoding speed of over 5.8 Gpix/s, which is much faster than most existing systems.

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Kong, Lingjun, Haiyang Liu, Wentao Hou, and Bin Dai. "Improving Decodability of Polar Codes by Adding Noise." Symmetry 14, no. 6 (June 3, 2022): 1156. http://dx.doi.org/10.3390/sym14061156.

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This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.

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Zhang, Yingxian, Aijun Liu, Xiaofei Pan, Shi He, and Chao Gong. "A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/606913.

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We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.

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Yao, Hanwen, Arman Fazeli, and Alexander Vardy. "List Decoding of Arıkan’s PAC Codes." Entropy 23, no. 7 (June 30, 2021): 841. http://dx.doi.org/10.3390/e23070841.

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Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. However, its performance at short blocklengths under standard successive cancellation decoding is far from optimal. A well-known way to improve the performance of polar codes at short blocklengths is CRC precoding followed by successive-cancellation list decoding. This approach, along with various refinements thereof, has largely remained the state of the art in polar coding since it was introduced in 2011. Recently, Arıkan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. One of our primary goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size L is moderately large (say, L⩾128). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed–Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as ν=2.

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Li, Huan, Jingxuan Huang, and Ce Sun. "Improved Belief Propagation List Decoding for Polar Codes." Electronics 11, no. 15 (August 7, 2022): 2458. http://dx.doi.org/10.3390/electronics11152458.

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Polar codes have become the channel coding scheme for control channel of enhanced mobile broadband in the 5G communication systems. Belief propagation (BP) decoding of polar codes has advantages of low decoding latency and high parallelism but achieves worse bit error ratio (BER) performance compared with the successive cancellation list (SCL) decoding scheme. In this paper, an improved BP list (IBPL) decoding algorithm is proposed with comparable BER performance to SCL algoritm. Firstly, the optimal permuted factor graph is analyzed for polar codes, which improves the performance of the BP decoder without path extension. Furthermore, based on the optimal graph, the bit metric and decoding path metric are proposed to extend and prune the decoding path. The proposed IBPL decoder is focused on not only the permutation of polar codes but also the reliabilities of decoded codewords during each iteration of BP decoding, which has a more accurate decoding path list. The simulation results show that the proposed IBPL decoder improves the BER performance compared with the original BP decoder significantly, and can approach the performance of the SCL decoder at low signal to noise ratio regions.

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Minja, Aleksandar, Dušan Dobromirov, and Vojin Šenk. "Bidirectional stack decoding of polar codes." Vojnotehnicki glasnik 69, no. 2 (2021): 405–15. http://dx.doi.org/10.5937/vojtehg69-29858.

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Introduction/purpose: The paper introduces a reduced latency stack decoding algorithm of polar codes, inspired by the bidirectional stack decoding of convolutional codes and based on the folding technique. Methods: The stack decoding algorithm (also known as stack search) that is useful for decoding tree codes, the list decoding technique introduced by Peter Elias and the folding technique for polar codes which is used to reduce the latency of the decoding algorithm. The simulation was done using the Monte Carlo procedure. Results: A new polar code decoding algorithm, suitable for parallel implementation, is developed and the simulation results are presented. Conclusions: Polar codes are a class of capacity achieving codes that have been adopted as the main coding scheme for control channels in 5G New Radio. The main decoding algorithm for polar codes is the successive cancellation decoder. This algorithm performs well at large blocklengths with a low complexity, but has very low reliability at short and medium blocklengths. Several decoding algorithms have been proposed in order to improve the error correcting performance of polar codes. The successive cancellation list decoder, in conjunction with a cyclic redundancy check, provides very good error-correction performance, but at the cost of a high implementation complexity. The successive cancellation stack decoder provides similar error-correction performance at a lower complexity. Future machine-type and ultra reliable low latency communication applications require high-speed low latency decoding algorithms with good error correcting performance. In this paper, we propose a novel decoding algorithm, inspired by the bidirectional stack decoding of classical convolutional codes, with reduced latency that achieves similar performance as the classical successive cancellation list and successive cancellation stack decoding algorithms. The results are presented analytically and verified by simulation.

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Wang, Xiumin, Jinlong He, Jun Li, and Liang Shan. "Reinforcement Learning for Bit-Flipping Decoding of Polar Codes." Entropy 23, no. 2 (January 30, 2021): 171. http://dx.doi.org/10.3390/e23020171.

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A traditional successive cancellation (SC) decoding algorithm produces error propagation in the decoding process. In order to improve the SC decoding performance, it is important to solve the error propagation. In this paper, we propose a new algorithm combining reinforcement learning and SC flip (SCF) decoding of polar codes, which is called a Q-learning-assisted SCF (QLSCF) decoding algorithm. The proposed QLSCF decoding algorithm uses reinforcement learning technology to select candidate bits for the SC flipping decoding. We establish a reinforcement learning model for selecting candidate bits, and the agent selects candidate bits to decode the information sequence. In our scheme, the decoding delay caused by the metric ordering can be removed during the decoding process. Simulation results demonstrate that the decoding delay of the proposed algorithm is reduced compared with the SCF decoding algorithm, based on critical set without loss of performance.

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10

Jan, Qasim, Shahid Hussain, Muhammad Furqan, Zhiwen Pan, Nan Liu, and Xiaohu You. "A Novel Flip-List-Enabled Belief Propagation Decoder for Polar Codes." Electronics 10, no. 18 (September 18, 2021): 2302. http://dx.doi.org/10.3390/electronics10182302.

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Due to the design principle of parallel processing, belief propagation (BP) decoding is attractive, and it provides good error-correction performance compared with successive cancellation (SC) decoding. However, its error-correction performance is still inferior to that of successive cancellation list (SCL) decoding. Consequently, this paper proposes a novel flip-list- (FL)-enabled belief propagation (BP) method to improve the error-correction performance of BP decoding for polar codes with low computational complexity. The proposed technique identifies the vulnerable channel log-likelihood ratio (LLR) that deteriorates the BP decoding result. The FL is utilized to efficiently identify the erroneous channel LLRs and correct them for the next BP decoding attempt. The preprocessed channel LLR through FL improves the error-correction performance with minimal flipping attempts and reduces the computational complexity. The proposed technique was compared with the state-of-the-art BP, i.e., BP bit-flip (BP-BF), generalized BP-flip (GBPF), cyclic redundancy check (CRC)-aided (CA-SCL) decoding, and ordered statistic decoding (OSD), algorithms. Simulation results showed that the FL-BP had an excellent block error rate (BLER) performance gain up to 0.7 dB compared with BP, BP-BF, and GBPF decoder. Besides, the computational complexity was reduced considerably in the high signal-to-noise ratio (SNR) regime compared with the BP-BF and GBPF decoding methods.

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11

Gao, Hua Meng, and He Liu. "Study on the Performance of CCSDS Standard Convolution Codes through Simulation." Applied Mechanics and Materials 651-653 (September 2014): 1949–52. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1949.

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the (15, 1/6) standard convolution codes of CCSDS is powerful and is often used for satellite communication, and it is also the fundamental of designing channel code for deep space communication. After analyzing the channel encoding and decoding solutions in previous spacecrafts, the performance of CCSDS (15, 1/6) standard convolution codes is researched. The encoding efficiency is get by simulating (15, 1/6) standard convolution codes’ encoding system in C translation environment. The decoding performance and encoding gain are get by simulating (15, 1/6) standard convolution codes’ decoding system in BPSK modulation, AGWN channel and Viterbi decoding scheme.

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Zhu, Peng, Jun Zhu, and Xiang Liu. "A Study on Turbo Code Performance Based on AWGN Channel." Applied Mechanics and Materials 347-350 (August 2013): 1720–26. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1720.

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Turbo codes have a wide range of applications in 3G mobile communications, deep-sea communications, satellite communications and other power constrained fields. In the paper, the Turbo Code Decoding Principle and several major decoding methods are introduced. Simulations of Turbo code performance under different parameters of AWGN channel are made and the effects of the different interleaving length, the number of iterations, and the decoding algorithm to Turbo code performance are also discussed in AWGN channel. Simulation results show that under the same signal-to-noise ratio, the more the number of iterations is, the longer the sequence of information is, and the more excellent decoding algorithm is, the better the performance of Turbo codes is.

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Shibue, Ryohei, and Fumiyasu Komaki. "Firing rate estimation using infinite mixture models and its application to neural decoding." Journal of Neurophysiology 118, no. 5 (November 1, 2017): 2902–13. http://dx.doi.org/10.1152/jn.00818.2016.

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Neural decoding is a framework for reconstructing external stimuli from spike trains recorded by various neural recordings. Kloosterman et al. proposed a new decoding method using marked point processes (Kloosterman F, Layton SP, Chen Z, Wilson MA. J Neurophysiol 111: 217–227, 2014). This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate intensity functions of marked point processes. However, the use of kernel density estimation causes problems such as low decoding accuracy and high computational costs. To overcome these problems, we propose a new decoding method using infinite mixture models to estimate intensity. The proposed method improves decoding performance in terms of accuracy and computational speed. We apply the proposed method to simulation and experimental data to verify its performance. NEW & NOTEWORTHY We propose a new neural decoding method using infinite mixture models and nonparametric Bayesian statistics. The proposed method improves decoding performance in terms of accuracy and computation speed. We have successfully applied the proposed method to position decoding from spike trains recorded in a rat hippocampus.

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14

Wang, Zhong-xun, Yang Xi, and Zhan-kai Bao. "Nonbinary Low-Density Parity Check Decoding Algorithm Research-Based Majority Logic Decoding." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (March 20, 2020): 2058016. http://dx.doi.org/10.1142/s0218001420580161.

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In the nonbinary low-density parity check (NB-LDPC) codes decoding algorithms, the iterative hard reliability based on majority logic decoding (IHRB-MLGD) algorithm has poor error correction performance. The essential reason is that the hard information is used in the initialization and iterative processes. For the problem of partial loss of information, when the reliability is assigned during initialization, the error correction performance is improved by modifying the assignment of reliability at initialization. The initialization process is determined by the probability of occurrence of the number of erroneous bits in the symbol and the Hamming distance. In addition, the IHRB-MLGD decoding algorithm uses the hard decision in the iterative decoding process. The improved algorithm adds soft decision information in the iterative process, which improves the error correction performance while only slightly increasing the decoding complexity, and improves the reliability accumulation process which makes the algorithm more stable. The simulation results indicate that the proposed algorithm has a better decoding performance than IHRB algorithm.

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A. Zikry, Abdel Halim, Ashraf Y. Hassan, Wageda I. Shaban, and Sahar F. Abdel-Momen. "Performance Analysis of LDPC Decoding Techniques." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 17–26. http://dx.doi.org/10.35940/ijrte.e5067.019521.

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Low density parity checking codes (LDPC) are one of the most important issues in coding theory at present. LDPC-code are a type of linear-block LDPC-codes. Channel coding might be considered as the finest conversant and most potent components of cellular communications systems, that was employed for transmitting errors corrections imposed by noise, fading and interfering. LDPC-codes are advanced coding gain, i.e., new area in coding. the performances of LDPC-code are similar to the Shannon-limiting, this led to the usage of decoding in several applications in digital communications systems, like DVB-S2 and WLAN802.1..This paper aims to know what is LDPC,what its application and introduce encoding algorithms that gives rise to a linear encoding time and also show that the regular and irregular LDPC performance and also introduce different methods for decoding LDPC. I discuss in detail LDPC decoding algorithm: bit flipping algorithm, as a type from hard decision .belief propagation algorithm, sum product algorithm and minimum sum algorithm as examples from soft decision .I expect that at least some students or researchers involved in researching LDPC codes would find this paper helpful.

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Sato-Reinhold, J., J. L. Sy, K. Das, J. C. Elliott, M. P. Eckstein, and B. Giesbrecht. "Neural decoding during continuous task performance." Journal of Vision 11, no. 11 (September 23, 2011): 197. http://dx.doi.org/10.1167/11.11.197.

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Wei Yu and Yun He. "A high performance cabac decoding architecture." IEEE Transactions on Consumer Electronics 51, no. 4 (November 2005): 1352–59. http://dx.doi.org/10.1109/tce.2005.1561867.

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Zhou, Fan, Linlin Niu, and Binghe Tian. "Performance analysis of LDPC decoding algorithm." Journal of Physics: Conference Series 1453 (January 2020): 012026. http://dx.doi.org/10.1088/1742-6596/1453/1/012026.

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Lubomir Kostal, Petr Lansky, and Stevan Pilarski. "Performance breakdown in optimal stimulus decoding." Journal of Neural Engineering 12, no. 3 (May 6, 2015): 036012. http://dx.doi.org/10.1088/1741-2560/12/3/036012.

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AlMahamdy, Mohammed, and Naser Al-Falahy. "Combining serial and parallel decoding for turbo codes." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 896. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp896-903.

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Reducing the decoding latency of the turbo codes is important to real-time applications. Conventionally, the decoding of the turbo codes (TC) runs in serial fashion, which means only one of the constituent soft decoders runs at a time. Parallel decoding (PD) refers to running the soft decoders in parallel. Although it delivers the output faster (compared to the serial decoding (SD)), it affects the bit- and frame-error rates. This paper proposes a decoding procedure that combines both PD and SD. It bridges the two decoding modes to determine the best combination scheme to achieve the required level of performance at an acceptable decoding latency. Presented results show how this procedure can mitigate the performance degradation at a slight increase in the decoding latency.

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Liao, Siyu, Chunhua Deng, Miao Yin, and Bo Yuan. "Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8574–82. http://dx.doi.org/10.1609/aaai.v35i10.17040.

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Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. By integrating both the residual input and residual learning to the design of neural channel decoder, DRN enables significant decoding performance improvement while maintaining low complexity. Extensive experiment results show that on different types of channel codes, our DRN decoder consistently outperform the state-of-the-art decoders in terms of decoding performance, model sizes and computational cost.

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Wang, Zhong Xun, and Xing Long Gao. "Design of Modified Minsum Decoder of LDPC Code in the Simplified Difference-Domain." Applied Mechanics and Materials 385-386 (August 2013): 1576–81. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1576.

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In this paper, we propose the modified minsum decoding algorithm of LDPC(Low-Density Parity-Check) code in the simplified difference-domain on the basis of detailed analysis of LDPC decoding algorithm in difference-domain. The simulation indicates that the proposed decoding algorithm offers almost no performance degradation compared with the BP(Belief Propagation) decoding algorithm in log-domain and the decoding algorithm in difference-domain and offers better performance than minsum decoding algorithm in log-domain and greatly reduces the computation complexity in AWGN(Additive White Gaussian Noise) channel and under BPSK(Binary Phase Shift Keying) modulation.

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Jin Ren. "Complexity/Performance Tradeoff: Asymmetrical Poly-diagonalization and Trellis Decoding & Sphere Decoding." International Journal of Digital Content Technology and its Applications 6, no. 23 (December 31, 2012): 261–70. http://dx.doi.org/10.4156/jdcta.vol6.issue23.30.

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Yan, Biao, Jia Min Wang, Ding Ping Guo, and Sai Yan Cai. "Analysis of MMSE-Based Anti-Interference Performance for MIMO-UWB Systems." Applied Mechanics and Materials 347-350 (August 2013): 1748–52. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1748.

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The combinations of MIMO (Multiple-Input-Multiple-Output) and UWB (Ultra-Wide-Band) systems greatly improve the performance of the UWB system. For the MIMO-UWB system, a new decoding scheme is proposed based on the STF (Space-Time-Frequency) coding and it can reduce the decoding complexity. The simulation results show that under the same SNR, the new method can obtain better performance of the bit error rate than the traditional ML decoding method.

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Zhang, Xianwen, Ming Jiang, Mingyang Zhu, Kailin Liu, and Chunming Zhao. "CRC-Aided Adaptive BP Decoding of PAC Codes." Entropy 24, no. 8 (August 22, 2022): 1170. http://dx.doi.org/10.3390/e24081170.

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Although long polar codes with successive cancellation decoding can asymptotically achieve channel capacity, the performance of short blocklength polar codes is far from optimal. Recently, Arıkan proposed employing a convolutional pre-transformation before the polarization network, called polarization-adjusted convolutional (PAC) codes. In this paper, we focus on improving the performance of short PAC codes concatenated with a cyclic redundancy check (CRC) outer code, CRC-PAC codes, since error detection capability is essential in practical applications, such as the polar coding scheme for the control channel. We propose an enhanced adaptive belief propagation (ABP) decoding algorithm with the assistance of CRC bits for PAC codes. We also derive joint parity-check matrices of CRC-PAC codes suitable for iterative BP decoding. The proposed CRC-aided ABP (CA-ABP) decoding can effectively improve error performance when partial CRC bits are used in the decoding. Meanwhile, the error detection ability can still be guaranteed by the remaining CRC bits and adaptive decoding parameters. Moreover, compared with the conventional CRC-aided list (CA-List) decoding, our proposed scheme can significantly reduce computational complexity, to achieve a better trade-off between the performance and complexity for short PAC codes.

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Kumar, H., Udupi Sripati, K. Shetty, and B. Shankarananda. "Soft Decision Fano Decoding of Block Codes Over Discrete Memoryless Channel Using Tree Diagram." Journal of Electrical Engineering 63, no. 1 (January 1, 2012): 59–64. http://dx.doi.org/10.2478/v10187-012-0009-y.

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Soft Decision Fano Decoding of Block Codes Over Discrete Memoryless Channel Using Tree DiagramA novel low complexity soft decision technique which allows the decoding of block codes with tree structure is proposed. These codes are shown to have a convenient tree structure that allows Fano decoding techniques to be used to decode them. The Fano algorithm searches through the tree structure of the block code for a path which has the optimal value of the Fano metric function. When a new candidate codeword is found, an optimality check is performed on it by using the threshold. If checked successfully, the candidate codeword is the most likely codeword and the search stops. The basic idea of this approach is to achieve a good error performance progressively in a minimum number of steps. For each decoding step, the error performance is tightly bounded and the decoding is terminated at the stage where either optimum or near optimum error performance is achieved. As a result, more flexibility in the trade off between performance and decoding complexity is provided. Some examples of the tree construction and the soft decision Fano decoding procedure are discussed.

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Chen, Rui Ping, Zhong Xun Wang, and Xin Qiao Yu. "A Min-Sum Algorithm Suitable for Hardware Implementation Based on LDPC Codes." Advanced Materials Research 271-273 (July 2011): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.458.

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Decoding algorithms with strong practical value not only have good decoding performance, but also have the computation complexity as low as possible. For this purpose, the paper points out the modified min-sum decoding algorithm(M-MSA). On the condition of no increasing in the decoding complexity, it makes the error-correcting performance improved by adding the appropriate scaling factor based on the min-sum algorithm(MSA), and it is very suitable for hardware implementation. Simulation results show that this algorithm has good BER performance, low complexity and low hardware resource utilization, and it would be well applied in the future.

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Park, Jinsoo, Gangsan Kim, Hong-Yeop Song, Chanki Kim, Jong-Seon No, and Suil Kim. "Analysis of Iterative Erasure Insertion and Decoding of FH/MFSK Systems without Channel State Information." Security and Communication Networks 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7318043.

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We analyze the symbol measures for iterative erasure insertion and decoding of a Reed-Solomon coded SFH/MFSK system over jamming channels. In contrast to conventional erasure insertion schemes, iterative schemes do not require any preoptimized threshold or channel state information at the receiver. We confirm the performance improvement using a generalized minimum distance (GMD) decoding method with three different symbol measures. To analyze performance, we propose a new analysis framework considering the “trapped-error” probability. From analysis and the simulation results, we show that ratio-based GMD decoding has the best performance among the one-dimensional iterative erasure insertion and decoding schemes.

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Wang, Li Na, and Wei Tang. "Decoding Improvement for LT Codes." Applied Mechanics and Materials 394 (September 2013): 499–504. http://dx.doi.org/10.4028/www.scientific.net/amm.394.499.

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In this paper the performance of LT codes is analyzed. And then, for the remaining part of information which can be decoded still exists when decoding failure, an improved message passing decoding algorithm which retains the original algorithm framework is proposed. The simulation results have shown that the improved message passing decoding algorithm improves the decoding rate and reduces decoding overhead on the premise of appropriately increasing decoding complexity.

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Zhou, Lingxia, Meixiang Zhang, Satya Chan, and Sooyoung Kim. "Review and Evaluation of Belief Propagation Decoders for Polar Codes." Symmetry 14, no. 12 (December 13, 2022): 2633. http://dx.doi.org/10.3390/sym14122633.

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The polar code has become one of the most popular and important forward error correction (FEC) coding schemes due to its symmetric characteristics of channel polarization. This paper reviews various decoding schemes for polar codes and discusses their advantages and disadvantages. After reviewing the existing performance-enhancing techniques such as belief propagation decoding with list, a new method is proposed to further improve the performance. In addition, a new complexity reduction technique based on the constituent codes is proposed, and a new scheduling scheme is introduced to reduce the decoding latency. Due to the recent development of neural networks, their applications to decoding schemes are also reviewed and evaluated. Finally, the proposed complexity-reduced technique is integrated with a neural network-based belief propagation decoding, which demonstrates performance enhancement as well as computational complexity reduction.

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Zhang, Meng, Zhuo Li, Lijuan Xing, and Xin Liao. "Higher-Order Belief Propagation Correction Decoder for Polar Codes." Entropy 24, no. 4 (April 11, 2022): 534. http://dx.doi.org/10.3390/e24040534.

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Belief propagation (BP) decoding for polar codes has been extensively studied because of its inherent parallelism. However, its performance remains inferior to that of successive cancellation list decoding (SCL) due to the structure of the decoding graph. To improve the block error rate (BLER) performance, the BP correction (BPC) decoding, a post-processing scheme that corrects prior knowledge of the identified code bit, improves convergence by executing additional iterations on the failed BP decoder. Moreover, the BPC decoder demonstrates a better decoding performance than the BP-based bit-flipping decoder. Nevertheless, the additional decoding attempts lead to increased latency. In this article, a modified BPC decoder is proposed to reduce the number of decoding attempts by redefining the correction rules. A new metric is designed to effectively identify the corrected location. Numerical results show that the proposed modified BPC decoder achieves a slight improvement in BLER compared with the original BPC, with a dramatic reduction in average complexity. Furthermore, a higher-order version, named MBPC-Ω, is extended to further improve the performance, where the Ω is the maximum correction order. Numerical results show that the higher-order modified BPC achieves a similar BLER performance to existing multiple bit-flipping BP decoders but has around half the latency overhead. In addition, the proposed MBPC-2 decoder performs better than the cyclic redundancy check-aided SCL (CA-SCL) decoder with list size 4 and is slightly worse than the CA-SCL with list size 8 in high signal-to-noise ratio (SNR) regions but with significant decoding latency reduction.

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Wang, Zhong Xun, Fang Qiang Zhu, Li Liu, and Juan Wang. "Loop Detection Based on Bit-Flipping Decoding Algorithm for LDPC Codes." Advanced Materials Research 271-273 (July 2011): 452–57. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.452.

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In this paper, we introduce a new bit-flipping decoding algorithm for low-density parity-check codes based on loop detection mechanism, which is an extension to soft-decision decoding. This decoding algorithm's performance has been significantly improved by introducing a loop detection mechanism for the failed flipping bit and leading into the soft-decision about the reliability measure of the received symbols. Theoretical analysis shows that the complexity of this algorithm is lower. Some simulation results are given, which show that compared with other known kinds of bit-flipping decoding algorithms for LDPC codes, this new decoding on the additive white Gaussian noise channel offer excellent performance.

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Xu, Jin, Ying Zhao, and Shu Qiang Duan. "Research and Realization by FPGA of Turbo Codes." Advanced Materials Research 588-589 (November 2012): 765–68. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.765.

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Turbo Code is a channel coding with excellent error-correcting performance in the condition of low noise-signal ratio.It has a superior decoding performance approaching the Shannon limit by adopting the random coding and decoding. This paper focuses on Turbo code and its implementation with FPGA and deeply analyzes the decoding theory and algorithm of Turbo code. Firstly, it analyzes the decoding theory of Turbo code. Then, it discusses key issues in the process of implementation with the most excellent and complicated Max—log—MAP algorithm. At last, it ends up with the Turbo encoder and decoding algorithm which hardware is successfully implemented finally.

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Cui, Lanlan, Fei Wu, Xiaojian Liu, Meng Zhang, Renzhi Xiao, and Changsheng Xie. "Improving LDPC Decoding Performance for 3D TLC NAND Flash by LLR Optimization Scheme for Hard and Soft Decision." ACM Transactions on Design Automation of Electronic Systems 27, no. 1 (January 31, 2022): 1–20. http://dx.doi.org/10.1145/3473305.

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Low-density parity-check (LDPC) codes have been widely adopted in NAND flash in recent years to enhance data reliability. There are two types of decoding, hard-decision and soft-decision decoding. However, for the two types, their error correction capability degrades due to inaccurate log-likelihood ratio (LLR) . To improve the LLR accuracy of LDPC decoding, this article proposes LLR optimization schemes, which can be utilized for both hard-decision and soft-decision decoding. First, we build a threshold voltage distribution model for 3D floating gate (FG) triple level cell (TLC) NAND flash. Then, by exploiting the model, we introduce a scheme to quantize LLR during hard-decision and soft-decision decoding. And by amplifying a portion of small LLRs, which is essential in the layer min-sum decoder, more precise LLR can be obtained. For hard-decision decoding, the proposed new modes can significantly improve the decoder’s error correction capability compared with traditional solutions. Soft-decision decoding starts when hard-decision decoding fails. For this part, we study the influence of the reference voltage arrangement of LLR calculation and apply the quantization scheme. The simulation shows that the proposed approach can reduce frame error rate (FER) for several orders of magnitude.

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Chen, Deguang, Ziping Ma, Lin Wei, Jinlin Ma, and Yanbin Zhu. "MTQA: Text-Based Multitype Question and Answer Reading Comprehension Model." Computational Intelligence and Neuroscience 2021 (February 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/8810366.

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Text-based multitype question answering is one of the research hotspots in the field of reading comprehension models. Multitype reading comprehension models have the characteristics of shorter time to propose, complex components of relevant corpus, and greater difficulty in model construction. There are relatively few research works in this field. Therefore, it is urgent to improve the model performance. In this paper, a text-based multitype question and answer reading comprehension model (MTQA) is proposed. The model is based on a multilayer transformer encoding and decoding structure. In the decoding structure, the headers of the answer type prediction decoding, fragment decoding, arithmetic decoding, counting decoding, and negation are added for the characteristics of multiple types of corpora. Meanwhile, high-performance ELECTRA checkpoints are employed, and secondary pretraining based on these checkpoints and an absolute loss function are designed to improve the model performance. The experimental results show that the performance of the proposed model on the DROP and QUOREF corpora is better than the best results of the current existing models, which proves that the proposed MTQA model has high feature extraction and relatively strong generalization capabilities.

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Yeom, Hong Gi, Wonjun Hong, Da-Yoon Kang, Chun Kee Chung, June Sic Kim, and Sung-Phil Kim. "A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/176857.

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Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.

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Chen, Weigang, Wenting Zhao, Hui Li, Suolei Dai, Changcai Han, and Jinsheng Yang. "Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation." Electronics 9, no. 1 (January 8, 2020): 122. http://dx.doi.org/10.3390/electronics9010122.

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Low-density parity-check (LDPC) codes have the potential for applications in future high throughput optical communications due to their significant error correction capability and the parallel decoding. However, they are not able to satisfy the very low bit error rate (BER) requirement due to the error floor phenomenon. In this paper, we propose a low-complexity iterative decoding scheme for product codes consisting of very high rate outer codes and LDPC codes. The outer codes aim at eliminating the residual error floor of LDPC codes with quite low implementation costs. Furthermore, considering the long simulation time of computer simulation for evaluating very low BER, the hardware platform is built to accelerate the evaluation of the proposed iterative decoding methods. Simultaneously, the fixed-point effects of the decoding algorithms are also be evaluated. The experimental results show that the iterative decoding of the product codes can achieve a quite low bit error rate. The evaluation using field programmable gate array (FPGA) also proves that product codes with LDPC codes and high-rate algebraic codes can achieve a good trade-off between complexity and throughput.

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McGlade, Nicola, Caragh Behan, Judy Hayden, Therese O'Donoghue, Rosie Peel, Farhan Haq, Michael Gill, Aiden Corvin, Eadbhard O'Callaghan, and Gary Donohoe. "Mental state decoding v. mental state reasoning as a mediator between cognitive and social function in psychosis." British Journal of Psychiatry 193, no. 1 (July 2008): 77–78. http://dx.doi.org/10.1192/bjp.bp.107.044198.

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SummaryTheory of mind deficits in schizophrenia have been parsed into mental state reasoning and mental state decoding components. We report that mental state decoding as measured by the ‘Eyes task’ better predicted social function than mental state reasoning as measured by the ‘Hinting task’ in 73 out-patients with chronic schizophrenia. Mental state decoding task performance also partly mediated the influence of basic neuropsychological performance on social function. We discuss these findings in terms of the accumulating evidence that mental state decoding has particular relevance for understanding deficits in social function in schizophrenia

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Alabed, Samer. "A computationally efficient detector for MIMO systems." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4138. http://dx.doi.org/10.11591/ijece.v9i5.pp4138-4146.

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<p>In this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques.</p>

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de Souza Sant'Ana, Jean Michel, Arliones Hoeller, Richard Demo Souza, Hirley Alves, and Samuel Montejo-Sanchez. "LoRa Performance Analysis with Superposed Signal Decoding." IEEE Wireless Communications Letters 9, no. 11 (November 2020): 1865–68. http://dx.doi.org/10.1109/lwc.2020.3006588.

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Chen, L., R. A. Carrasco, and M. Johnston. "List decoding performance of algebraic geometric codes." Electronics Letters 42, no. 17 (2006): 986. http://dx.doi.org/10.1049/el:20061999.

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Leanderson, C. F., and C. E. W. Sundberg. "Performance Evaluation of List Sequence MAP Decoding." IEEE Transactions on Communications 53, no. 3 (March 2005): 422–32. http://dx.doi.org/10.1109/tcomm.2005.843426.

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El-Khamy, Mostafa, Haris Vikalo, Babak Hassibi, and Robert J. McEliece. "Performance of sphere decoding of block codes." IEEE Transactions on Communications 57, no. 10 (October 2009): 2940–50. http://dx.doi.org/10.1109/tcomm.2009.10.080402.

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Sankaraiah, S., and C. Eswaran. "Performance optimization of real-time video decoding." Computers & Electrical Engineering 70 (August 2018): 366–79. http://dx.doi.org/10.1016/j.compeleceng.2016.08.022.

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Takpaya, Kazi. "Performance of three stage turbo-equalization-decoding." Journal of Electronics (China) 20, no. 6 (November 2003): 439–45. http://dx.doi.org/10.1007/s11767-003-0058-y.

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Wang, Li Na, and Xiao Liu. "Improved BP Decoding Algorithm for LDPC Codes." Advanced Materials Research 846-847 (November 2013): 925–28. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.925.

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In this paper, an improved belief propagation decoding algorithm was proposed for low density parity check codes. In the proposed decoding process, error bits can be detected once again after hard-decision in the conventional BP decoding algorithm. The detection criterion is based on check matrix characteristics and D-value between prior probability and posterior probability. Simulation results demonstrate the performance of the improved BP decoding algorithm outperform that of the conventional BP decoding algorithm.

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Cho, Hyun Woo, and Young Joon Song. "High Speed Decoding for High-Rate and Short-Length Reed–Muller Code Using Auto-Decoder." Applied Sciences 12, no. 18 (September 14, 2022): 9225. http://dx.doi.org/10.3390/app12189225.

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In this paper, we show that applying a machine learning technique called auto-decoder (AD) to high-rate and short length Reed–Muller (RM) decoding enables it to achieve maximum likelihood decoding (MLD) performance and faster decoding speed than when fast Hadamard transform (FHT) is applied in additive white Gaussian noise (AWGN) channels. The decoding speed is approximately 1.8 times and 125 times faster than the FHT decoding for R(1,4) and R(2,4), respectively. The number of nodes in the hidden layer of AD is larger than that of the input layer, unlike the conventional auto-encoder (AE). Two ADs are combined in parallel and merged together, and then cascaded to one fully connected layer to improve the bit error rate (BER) performance of the code.

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Ammari, Mohamed Lassaad. "Performance of Blind Turbo Equalizer in Indoor Channels." Journal of Communications Software and Systems 7, no. 2 (June 22, 2011): 52. http://dx.doi.org/10.24138/jcomss.v7i2.178.

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In this paper, we consider the transmission of turbo coded symbols in the indoor radio environment. The system isaffected by the intersymbol interference (ISI) caused by the multipath time-delay spread of the transmission medium. To reduce the channel effect, we propose to use a blind turbo equalizer combining channel estimation, equalization and turbo decoding. The equalizer consists of an interference canceller (IC) and a MAP-BCJR decoder. To improve system performances, we redefine the channel reliability factor used by the MAP-BCJR decoding algorithm. We propose a new metric that takes into account the statistics of the signal at the equalizer output. The channel coefficient estimation is performed using a recursive least squares (RLS) algorithm. A blind receiver initializationtechnique is proposed. This technique is based on a soft decisiondirected least mean square algorithm (soft DD-LMS). For the proposed turbo detector, the ISI cancellation, channel estimation and decoding are jointly performed through an iterative process where modules exchange a soft information.

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Song, Hongzhan, Shangsheng Wen, Chen Yang, Danlan Yuan, and Weipeng Guan. "Universal and Effective Decoding Scheme for Visible Light Positioning Based on Optical Camera Communication." Electronics 10, no. 16 (August 10, 2021): 1925. http://dx.doi.org/10.3390/electronics10161925.

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As a promising approach to implement indoor positioning, visible light positioning (VLP) based on optical camera communication (OCC) image sensor has attracted substantial attention. However, the decoding schemes of existing VLP systems still face many challenges. First, the transmission channel between transmitters and receivers can be easily affected by environmental changes, resulting in poor thresholding performance. Second, the inherently unsynchronized air transmission channel issue remains a big obstacle for decoding data. The above two problems limit the application of VLP systems, where various mobile devices are used as receivers and the properties of transmission channel are constantly changing with the movement of receivers. In this paper, a universal and effective decoding scheme named pixel-to-bit calculation (PBC) decoding algorithm for VLP systems is proposed and experimentally demonstrated. It includes a Staged Threshold Scheme which provides excellent thresholding performance for different transmission channel conditions, as well as a Synchronous Decoding Operation to automatically synchronize the clock between transmitters and receivers. A decoding rate of 95.62% at the height of 2.73 m is realized in a practical Robotic-based VLP system embedded with our proposed PBC decoding scheme. In addition, experimental results show that the average decoding rate of the proposed PBC decoding scheme reaches 99.9% when applying different transmitters and receivers.

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Beeharry, Yogesh, Tulsi Pawan Fowdur, and Krishnaraj M. S. Soyjaudah. "SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES." IIUM Engineering Journal 18, no. 1 (May 30, 2017): 111–31. http://dx.doi.org/10.31436/iiumej.v18i1.661.

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This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding.

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Journal articles on the topic 'Decoding performance'

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