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Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Decoding performance"

1

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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Анотація:
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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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Дисертації з теми "Decoding performance"

1

Lee, L. H. C. "Convolutional code design and performance." Thesis, University of Manchester, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382770.

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2

Li, Xin. "Collaborative decoding and its performance analysis." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0013385.

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3

Granke, Daniel. "Decoding Acting Vocabulary." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3110.

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This paper compares seemingly similar words from a variety of acting teachers, and shows how it is impossible to draw clear comparisons between words that are often used as synonyms. The paper is a reflection of the journey from believing in translation to recognizing its impossibility. In Chapter 1 we focus on one of the most common elements in actor training, Attention/focus/concentration, and analyze the shades of meaning in those words and the difficulty of talking about them in isolation. In Chapter 2 we look at the way in which semiotic analysis can explain the words resistance to equivalence. In Chapter 3 we look at one of the central terms in most collegiate actor training objective, and see how it reveals both the problems inherent in translation. In Chapter 4 we look at how this knowledge can influence the classroom in a positive way.
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4

Krishnamurthi, Sumitha. "Performance of Recursive Maximum Likelihood Turbo Decoding." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1070481352.

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5

CONDO, CARLO. "VLSI decoding architectures: flexibility, robustness and performance." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2544356.

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Stemming from previous studies on flexible LDPC decoders, this thesis work has been mainly focused on the development of flexible turbo and LDPC decoder designs, and on the narrowing of the power, area and speed gap they might present with respect to dedicated solutions. Additional studies have been carried out within the field of increased code performance and of decoder resiliency to hardware errors. The first chapter regroups several main contributions in the design and implementation of flexible channel decoders. The first part concerns the design of a Network-on-Chip (NoC) serving as an interconnection network for a partially parallel LDPC decoder. A best-fit NoC architecture is designed and a complete multi-standard turbo/LDPC decoder is designed and implemented. Every time the code is changed, the decoder must be reconfigured. A number of variables influence the duration of the reconfiguration process, starting from the involved codes down to decoder design choices. These are taken in account in the flexible decoder designed, and novel traffic reduction and optimization methods are then implemented. In the second chapter a study on the early stopping of iterations for LDPC decoders is presented. The energy expenditure of any LDPC decoder is directly linked to the iterative nature of the decoding algorithm. We propose an innovative multi-standard early stopping criterion for LDPC decoders that observes the evolution of simple metrics and relies on on-the-fly threshold computation. Its effectiveness is evaluated against existing techniques both in terms of saved iterations and, after implementation, in terms of actual energy saving. The third chapter portrays a study on the resilience of LDPC decoders under the effect of memory errors. Given that the purpose of channel decoders is to correct errors, LDPC decoders are intrinsically characterized by a certain degree of resistance to hardware faults. This characteristic, together with the soft nature of the stored values, results in LDPC decoders being affected differently according to the meaning of the wrong bits: ad-hoc error protection techniques, like the Unequal Error Protection devised in this chapter, can consequently be applied to different bits according to their significance. In the fourth chapter the serial concatenation of LDPC and turbo codes is presented. The concatenated FEC targets very high error correction capabilities, joining the performance of turbo codes at low SNR with that of LDPC codes at high SNR, and outperforming both current deep-space FEC schemes and concatenation-based FECs. A unified decoder for the concatenated scheme is subsequently proposed.
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6

Han, Junsheng. "Code representation and performance of graph-Based decoding." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3297580.

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Thesis (Ph. D.)--University of California, San Diego, 2008.
Title from first page of PDF file (viewed April 28, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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7

Abedi, Ali. "Invariance Properties and Performance Evaluation of Bit Decoding Algorithms." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/768.

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Certain properties of optimal bitwise APP (A Posteriori Probability) decoding of binary linear block codes are studied. The focus is on the Probability Density Function (pdf) of the bit Log-Likelihood-Ratio (LLR). A general channel model with discrete (not necessarily binary) input and discrete or continuous output is considered. It is proved that under a set of mild conditions on the channel, the pdf of the bit LLR of a specific bit position is independent of the transmitted code-word. It is also shown that the pdf of a given bit LLR, when the corresponding bit takes the values of zero and one, are symmetric with respect to each other (reflection of one another with respect to the vertical axis). In the case of channels with binary inputs, a sufficient condition for two bit positions to have the same pdf is presented. An analytical method for approximate performance evaluation of binary linear block codes using an Additive White Gaussian Noise (AWGN) channel model with Binary Phase Shift Keying (BPSK) modulation is proposed. The pdf of the bit LLR is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit LLR. An analytical method for calculating these moments which is based on some recursive calculations involving certain weight enumerating functions of the code is introduced. It is proved that the approximation can be as accurate as desired, using enough numbers of terms in the Gram-Charlier series expansion. A new method for the performance evaluation of Turbo-Like Codes is presented. The method is based on estimating the pdf of the bit LLR by using an exponential model. The moment matching method is combined with the maximum entropy principle to estimate the parameters of the new model. A simple method is developed for computing the Probabilities of the Point Estimates (PPE) for the estimated parameters, as well as for the Bit Error Rate (BER). It is demonstrated that this method requires significantly fewer samples than the conventional Monte-Carlo (MC) simulation.
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8

Leong, Chi Wa. "Reduced complexity decoding and relative performance of turbo codes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq23379.pdf.

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9

Sankaranarayanan, Sundararajan. "Iterative Decoding of Codes on Graphs." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/194618.

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The growing popularity of a class of linear block codes called the low-density parity-check (LDPC) codes can be attributed to the low complexity of the iterative decoders, and their potential to achieve performance very close to the Shannon capacity. This makes them an attractive candidate for ECC applications in communication systems. This report proposes methods to systematically construct regular and irregular LDPC codes.A class of regular LDPC codes are constructed from incidence structures in finite geometries like projective geometry and affine geometry. A class of irregular LDPC codes are constructed by systematically splitting blocks of balanced incomplete block designs to achieve desired weight distributions. These codes are decoded iteratively using message-passing algorithms, and the performance of these codes for various channels are presented in this report.The application of iterative decoders is generally limited to a class of codes whose graph representations are free of small cycles. Unfortunately, the large class of conventional algebraic codes, like RS codes, has several four cycles in their graph representations. This report proposes an algorithm that aims to alleviate this drawback by constructing an equivalent graph representation that is free of four cycles. It is theoretically shown that the four-cycle free representation is better suited to iterative erasure decoding than the conventional representation. Also, the new representation is exploited to realize, with limited success, iterative decoding of Reed-Solomon codes over the additive white Gaussian noise channel.Wiberg, Forney, Richardson, Koetter, and Vontobel have made significant contributions in developing theoretical frameworks that facilitate finite length analysis of codes. With an exception of Richardson's, most of the other frameworks are much suited for the analysis of short codes. In this report, we further the understanding of the failures in iterative decoders for the binary symmetric channel. The failures of the decoder are classified into two categories by defining trapping sets and propagating sets. Such a classification leads to a successful estimation of the performance of codes under the Gallager B decoder. Especially, the estimation techniques show great promise in the high signal-to-noise ratio regime where the simulation techniques are less feasible.
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10

Yang, Tao Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Performance of iterative detection and decoding for MIMO-BICM systems." Awarded by:University of New South Wales. School of Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/26197.

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Multiple-input multiple-output (MIMO) wireless technology is an emerging cost- effective approach to offer multiple-fold capacity improvement relative to the conven- tional single-antenna systems. To achieve the capacities of MIMO channels, MIMO bit-interleaved-coded-modulation (BICM) systems with iterative detection and decod- ing (IDD) are studied in this thesis. The research for this dissertation is conducted based on the iterative receivers with convolutional codes and turbo codes. A variety of MIMO detectors, such as a maximum a posteriori probability (MAP) detector, a list sphere detector (LSD) and a parallel interference canceller (PIC) together with a decision statistic combiner (DSC), are studied. The performance of these iterative receivers is investigated via bounding techniques or Monte-Carlos simulations. Moreover, the computational complexities of the components are quantified and compared. The convergence behaviors of the iterative receivers are analyzed via variance trans- fer (VTR) functions and variance exchange graphs (VEGs). The analysis of conver- gence behavior facilitates the finding of components with good matching. For a fast fading channel, we show that the "waterfall region" of an iterative receiver can be predicted by VEG. For a slow fading channel, it is shown that the performance of an iterative receiver is essentially limited by the early interception ratio (ECR) which is obtained via simulations. After the transfer properties of the detectors are unveiled, a detection switching (DSW) methodology is proposed and the switching criterion based on cross entropy (CE) is derived. By employing DSW, the performance of an iterative receiver with a list sphere detector (LSD) of a small list size is considerably improved. It is shown that the iterative receiver achieves a performance very close to that with a maximum a posteriori probability (MAP) detector but with a significantly reduced complexity. For an iterative receiver with more than two components, various iteration sched- ules are explored. The schedules are applied in an iterative receiver with PIC-DSC. It is shown that the iterative receiver with a periodic scheduling outperforms that with the conventional scheduling at the same level of complexity.
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Книги з теми "Decoding performance"

1

Shu, Lin. Soft-decision decoding techniques for linear block codes and their error performance analysis. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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2

Shu, Lin. Soft-decision decoding techniques for linear block codes and their error performance analysis. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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3

United States. National Aeronautics and Space Administration., ed. Soft-decision decoding techniques for linear block codes and their error performance analysis. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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4

Shu, Lin. Soft-decision decoding techniques for linear block codes and their error performance analysis. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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5

Castura, Jeff. Performance analysis and optimization of reduced complexity low density parity check decoding algorithms. Ottawa: National Library of Canada, 2000.

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6

Xinwen, Wu, Rao, T. R. N. 1933-, and United States. National Aeronautics and Space Administration., eds. New double-byte error-correcting codes for memory systems. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Xinwen, Wu, Rao, T. R. N. 1933-., and United States. National Aeronautics and Space Administration., eds. New double-byte error-correcting codes for memory systems. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Xinwen, Wu, Rao, T. R. N. 1933-, and United States. National Aeronautics and Space Administration., eds. New double-byte error-correcting codes for memory systems. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Xinwen, Wu, Rao, T. R. N. 1933-, and United States. National Aeronautics and Space Administration., eds. New double-byte error-correcting codes for memory systems. [Washington, DC: National Aeronautics and Space Administration, 1996.

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10

G, Seetharaman, Feng G. L, and United States. National Aeronautics and Space Administration., eds. Communications and information research: Improved space link performance via concatenated forward error correction coding : program report on NASA subcontract. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Частини книг з теми "Decoding performance"

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Paire, J. T., P. Coulton, and P. G. Farrell. "Graph Configurations and Decoding Performance." In Cryptography and Coding, 158–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45325-3_15.

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Tomlinson, Martin, Cen Jung Tjhai, Marcel A. Ambroze, Mohammed Ahmed, and Mubarak Jibril. "Bounds on Error-Correction Coding Performance." In Error-Correction Coding and Decoding, 3–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51103-0_1.

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Tomlinson, Martin, Cen Jung Tjhai, Marcel A. Ambroze, Mohammed Ahmed, and Mubarak Jibril. "Soft and Hard Decision Decoding Performance." In Error-Correction Coding and Decoding, 25–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51103-0_2.

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Trujillo, Rafael A., Antonio M. Vidal, Víctor M. García, and Alberto González. "Parallelization of Sphere-Decoding Methods." In High Performance Computing for Computational Science - VECPAR 2008, 2–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92859-1_2.

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Falcão, Gabriel, Leonel Sousa, Vitor Silva, and José Marinho. "Parallel LDPC Decoding on the Cell/B.E. Processor." In High Performance Embedded Architectures and Compilers, 389–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92990-1_28.

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Azevedo, Arnaldo, Cor Meenderinck, Ben Juurlink, Andrei Terechko, Jan Hoogerbrugge, Mauricio Alvarez, and Alex Ramirez. "Parallel H.264 Decoding on an Embedded Multicore Processor." In High Performance Embedded Architectures and Compilers, 404–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92990-1_29.

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Knowles, Ric. "Encoding/Decoding Shakespeare: Richard III at the 2002 Stratford Festival." In A Companion to Shakespeare and Performance, 297–318. Oxford, UK: Blackwell Publishing Ltd, 2007. http://dx.doi.org/10.1002/9780470996706.ch17.

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Benoit, Mylène, and Philippe Guisgand. "Decoding Effet Papillon, a Choreography for Three Dancers Inspired by the World of Video Games." In Performance and Posthumanism, 127–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74745-9_7.

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Akanksha, Gupta, Jain Anjana, and Vyavahare Prakash. "Performance Analysis and Comparison of Various Channel Decoding Techniques." In Smart Computing and Informatics, 443–51. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5544-7_43.

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Faham, Hamza, My Seddiq El Kasmi Alaoui, Saïd Nouh, and Mohamed Azzouazi. "High Performance Decoding by Combination of the Hartmann Rudolph Decoder and Soft Decision Decoding by Hash Techniques." In Digital Technologies and Applications, 781–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73882-2_71.

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Тези доповідей конференцій з теми "Decoding performance"

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Jia Li, Gaigai Yang, and Zhiqiang Zhao. "An improved-performance decoding algorithm of LDPC codes for layered decoding." In 2014 IEEE International Conference on Communication Problem-Solving (ICCP). IEEE, 2014. http://dx.doi.org/10.1109/iccps.2014.7062283.

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Bayanifar, Mahdi, Robert Calderbank, and Olav Tirkkonen. "Performance Analysis of Binary Chirp Decoding." In 2023 IEEE Information Theory Workshop (ITW). IEEE, 2023. http://dx.doi.org/10.1109/itw55543.2023.10161617.

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Salim, Mohammad, and Aditya Bohra. "Performance improvement of Viterbi and Sova decoding Algorithms with finite decoding length." In 2008 International Conference on Recent Advances in Microwave Theory and Applications (MICROWAVE). IEEE, 2008. http://dx.doi.org/10.1109/amta.2008.4763077.

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Heo, J., and K. M. Chugg. "Constrained iterative decoding: performance and convergence analysis." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986919.

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Elzeiny, Sondos, Phoebe Edward, and Tallal Elshabrawy. "LoRa Performance Enhancement through List Decoding Technique." In 2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2021. http://dx.doi.org/10.1109/iccworkshops50388.2021.9473517.

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Pramanik, Ankita, Soma Sarkar, and Santi P. Maity. "Performance comparison of orthogonal complex MIMO STBC with ML decoding and soft decision decoding." In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2017. http://dx.doi.org/10.1109/wispnet.2017.8299936.

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Best, Matthew D., Kazutaka Takahashi, and Nicholas G. Hatsopoulos. "Comparing decoding performance between functionally defined neural populations." In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2015. http://dx.doi.org/10.1109/ner.2015.7146545.

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Janulewicz, Emil, and Amir H. Banihashemi. "Performance analysis of iterative decoding algorithms with memory." In 2010 IEEE Information Theory Workshop on Information Theory (ITW). IEEE, 2010. http://dx.doi.org/10.1109/itwksps.2010.5503202.

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Potetsianakis, Emmanouil, and Emmanuel Thomas. "Video Decoding Performance and Requirements for XR Applications." In MMSys '23: 14th Conference on ACM Multimedia Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3587819.3593940.

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Benkhelifa, Fatma, Zouheir Rezki, and Mohamed-Slim Alouini. "Cooperative decoding in femtocell networks: Performance-complexity tradeoff." In 2012 IEEE 13th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2012). IEEE, 2012. http://dx.doi.org/10.1109/spawc.2012.6292937.

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Звіти організацій з теми "Decoding performance"

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McEliece, Robert, and Padhraic Smyth. Turbo Decoding of High Performance Error-Correcting Codes via Belief Propagation. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada386835.

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Haney, Michael W. The Application of Free-Space Optical Interconnects to High Performance Communication Decoding. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada373463.

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Decoding Talent : Talent Management in the Indian Social Sector. Development Management Foundation, September 2022. http://dx.doi.org/10.58178/202219832016.

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Анотація:
Talent management (TM) has a direct association with organisational performance. In SPOs, this association is of greater strategic importance as the human hand cannot be replaced by technology or greater financial resources. Thus, for any SPO, achieving its mission heavily relies on its personnel's talent, which is "the total of all the experience, knowledge, skills, and behaviours that a person has and brings to work." However, the exploration of TM in SPOs is largely uncharted territory.
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