Unmanned aerial vehicle (UAV) ultraviolet (UV) communication has garnered significant attention due to its broad range of applications. Currently, there are few decoding algorithms specifically tailored for UAV UV mul...
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Unmanned aerial vehicle (UAV) ultraviolet (UV) communication has garnered significant attention due to its broad range of applications. Currently, there are few decoding algorithms specifically tailored for UAV UV multiple-input-single-output (MISO) communication scenarios. We propose an enhanced maximum a posteriori (MAP) and log-likelihood ratio belief propagation (LLRBP) algorithm for MISO UV communication systems. This algorithm strategically ranks interference signals based on the signal-to-interference plus noise ratio (SINR), effectively eliminates errors in transmitter information recovery, and is named the SINR-MAP-LLRBP algorithm. Building upon this algorithm, we establish the optimal quantitative relationship for the signal-to-noise ratio (SNR) in the MISO UVC system. Additionally, we introduce an enhanced decoding algorithm for MISO UV communication systems, leveraging the residual-based loop iteration methodology, named as the Res-SINR-MAP-LLRBP algorithm. This algorithm provides an additional decoding gain compared to the SINR-MAP-LLRBP algorithm.
Low-density parity check digit is a linear block code, which is applied to semiconductor storage to achieve effective information storage. However, in semiconductor storage, the existing ways have weak decoding perfor...
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Low-density parity check digit is a linear block code, which is applied to semiconductor storage to achieve effective information storage. However, in semiconductor storage, the existing ways have weak decoding performance and high bit error rate. Therefore, the decoding algorithm in the existing low-density parity check digit is optimised. Then, a semiconductor storage decoding optimisation algorithm based on artificial neural networks is designed to improve decoding performance. In addition, a multi-layer perceptron neural network is introduced to construct an optimised decoding method. The experimental results showed that when the threshold was 5, the throughput of the low-density parity check decoding algorithm based on the multi-layer perceptron decreased slowly with the increase in the signal-to-noise ratio. The minimum throughput rate was 1.18. The proposed semiconductor storage decoding optimisation algorithm based on artificial neural networks has better decoding performance. In summary, the proposed decoding optimisation method for semiconductor storage has good performance, which can effectively achieve information decoding in semiconductor storage.
This paper investigates non-binary quantum codes correcting multiple insertion errors. This paper provides an insertion-correcting procedure of the deletion-correcting non-binary codes constructed by Matsumoto and Hag...
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This paper investigates non-binary quantum codes correcting multiple insertion errors. This paper provides an insertion-correcting procedure of the deletion-correcting non-binary codes constructed by Matsumoto and Hagiwara. By giving the procedure, we present multiple- insertion-correcting non-binary quantum codes.
Reed-Solomon (RS) codes are widely used to correcter rors and erasures. This letter proposes a fast error and erasurede coding algorithm for RS codes. It achieves the best-known complexity O(nlog(n-k)+(n-k) log(2)(n-k...
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Reed-Solomon (RS) codes are widely used to correcter rors and erasures. This letter proposes a fast error and erasurede coding algorithm for RS codes. It achieves the best-known complexity O(nlog(n-k)+(n-k) log(2)(n-k)), wheren, kare the code length and dimension, respectively. Furthermore, the proposed method is efficient for practical codes. For decoding RS(255,223), compared with the existing method, the newalgorith m saves32%field operations
The wide application of the Internet of Things (IoT), large-scale satellite clusters and drone clusters requires multiple access capabilities for massive devices. In order to meet the high-capacity and low-latency acc...
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The wide application of the Internet of Things (IoT), large-scale satellite clusters and drone clusters requires multiple access capabilities for massive devices. In order to meet the high-capacity and low-latency access requirements in application scenarios such as the IoT, this paper proposes to apply sparse code multiple access (SCMA) technology to the massive terminal access of the IoT, focusing on the study of SCMA decoding algorithm. Aiming at the high complexity of its traditional decoding algorithm, a decoding algorithm based on codeword reliability is proposed. The algorithm reduces the number of codeword iterations with higher reliability by setting a threshold to reduce the algorithm complexity. The simulation results show that the algorithm reduces the complexity of the algorithm while the loss of bit error rate performance is very small. Then, aiming at the poor bit error rate performance of SCMA technology under the condition of low signal-to-noise ratio, a joint coding and decoding simulation link based on soft input and soft output Turbo code and SCMA technology is proposed. The simulation results prove that Turbo codes can speed up the convergence speed of the decoding algorithm. When the SNR is 4 dB, the bit error rate of the joint system has been reduced to below 10e-3, which has obvious advantages compared with the SCMA system without Turbo coding. Therefore, the two methods proposed in this paper can be used for the high-capacity demand of the IoT communication system.
Quantum low-density parity-check (QLDPC) codes have been considered as a promising solution for the fault-tolerant quantum computing. However, the belief propagation (BP) decoding for QLDPC codes does not take into ac...
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ISBN:
(纸本)9798350321814
Quantum low-density parity-check (QLDPC) codes have been considered as a promising solution for the fault-tolerant quantum computing. However, the belief propagation (BP) decoding for QLDPC codes does not take into account the degeneracy, resulting in certain performance degradation. Recently, various post-processing algorithms have been proposed to address this issue but they in return require excessive additional complexity and/or long decoding latency. Motivated by this, in this paper, we propose an efficient decoding algorithm that takes reduced decoding complexity for the post-processing. In particular, the proposed algorithm performs the post-processing in such a way that it first selects a variable node based on a proposed metric, and the depolarizing channel model for the selected variable node is reinitialized according to the BP decoding results. Then, the BP decoding is performed for the depolarizing channel model in which the selected node follows the reinitialized channel characteristics. The process of selection, reinitialization and BP decoding, say a trial, is iterated until it reaches a predetermined value or all syndromes are met. The main ideas of this work lie in formulating the metric for selection and reinitialization to minimize the number of trials until the degeneracy is resolved. Finally, simulation results show that the proposed decoding algorithms can significantly reduce the decoding complexity with similar decoding performance to that of an existing algorithm.
The performance of decoding algorithm is one of the important influential factors to determine the communication quality of optical camera communication(OCC) system. In this paper, we first propose a decoding algorith...
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The performance of decoding algorithm is one of the important influential factors to determine the communication quality of optical camera communication(OCC) system. In this paper, we first propose a decoding algorithm with adaptive thresholding based on the captured pixel values under an ideal environment, and then we further propose a decoding algorithm with multiple features, which is more suitable under the existence of the interference of light sources. The algorithm firstly determines the light-emitting diode(LED) array profile information by removing the interfering light sources through geometric features, and then identifies the LED state by calculating two grayscale features, the average gray ratio(AGR) and the gradient radial inwardness(GRI) of the LEDs, and finally obtains the LED state matrix. The experimental results show that the bit error ratio(BER) of the decoding algorithm with multiple features decreases from 1×10^(-2) to 5×10^(-4) at 80 m.
At present, while there are many conveniences in people's lives, lots of problem have also appeared with the development of information technology. In order to solve the problem of increasingly serious intellectua...
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At present, while there are many conveniences in people's lives, lots of problem have also appeared with the development of information technology. In order to solve the problem of increasingly serious intellectual property disputes, the research on digital watermarking is particularly important. Due to the digital watermarking for real-time requirement is not high. We improve the decoding algorithm in response to the oscillating iterative phenomenon that occurs during the LDPC code decoding algorithm;and the improved decoding algorithm has been introduced into the digital watermark technology. Finally, the simulation is realized through MATLAB, the results show that the improved LDPC code decoding algorithm significantly improves the robustness of digital watermarking obviously.
Our aim is the design of an efficient decoding algorithm in group codes. The algorithm is inspired by the well known syndrome decoding algorithm for linear codes and uses the decomposition of a semisimple group algebr...
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Our aim is the design of an efficient decoding algorithm in group codes. The algorithm is inspired by the well known syndrome decoding algorithm for linear codes and uses the decomposition of a semisimple group algebra KG as a direct sum of two-sided ideals, each of them generated by a central idempotent of KG. When G is an abelian group the algorithm can be modified to make it very simple and efficient. Some illustrative examples are presented.(c) 2023 Elsevier Inc. All rights reserved.
The quality of wireless channels between mobile vehicles can vary greatly, resulting in significant differences in the quality of received symbols. Additionally, Spinal codes decode sequentially, which means that a si...
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ISBN:
(纸本)9798400708909
The quality of wireless channels between mobile vehicles can vary greatly, resulting in significant differences in the quality of received symbols. Additionally, Spinal codes decode sequentially, which means that a single poor quality symbol can cause the entire message to fail to decode. To address this issue, a dynamic stack decoding algorithm is proposed in this paper. By leveraging equalization results at the receiver, the decoder can make approximate assessments of symbol quality, enabling targeted adjustments of the search range for each layer. This approach reduces decoding redundancy by narrowing the search range for layers with better received symbol quality, while expanding the search range for layers with poorer quality, making it easier to capture the correct path. Simulation results validate the effectiveness of the proposed algorithm in improving rate performance and concurrently reducing computation cost.
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