• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.83-89
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• 2013

An adaptive blind equalization is a technique using to minimize the Inter-symbol Interference occurred on a communication channel in the transmission of the high speed digital data. In this paper, we propose a blind equalization more improving performance of the conventional MSAGF-MMA adaptive blind equalization algorithm by applying a multiple step size. This algorithm apply a LMS algorithm with a several step size according to each region divided by absolute values of decision-directed error to MSAGF-MMA. By computer simulation, it is confirmed that the proposed algorithm has a performance highly enhanced in terms of a convergence speed, a residual ISI and a residual error and an ensemble averaged MSE in a steady status compared with MMA and MSAGF-MMA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1453-1459
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• 2000

In this paper, the method which compensates the problem occurred in case M-ary QAM is applied to system is proposed. The conventional CMA has two problems, First, when M is larger than 4, it has a poor performance of equalizer due to a degradation of convergence property. Second, the phase of conventional CMA is distorted after convergence. To compensate these problems, we set the proper interval according to modulated signal when the signal using 16-QAM modulation method is equalized and use a different equalizing method for each interval. Using this method, the ISI is reduced and the performance of equalizing is improved. Also, the computer simulation using residual ISI shows an improved performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2627-2647
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• 2022

The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.61-66
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• 2021

This paper related with the performance comparison of mSE-MMA and mDSE-MMA adaptive equalization algorithm which is possible to reduce the intersymbol interference that occurs in the nonlinear communication channel transmitting 16-QAM signal. This two algorithm is possible to reduce the computational load compared to the current MMA algorithm, it has the degraded equalization performance due to simplified arithmetic in order to applying the mobile communication terminal. In order to improve the performance degradation, they controls the step size according to the existence of arbitrary radius circle of equalizer output compared to transmitted symbol point. The variation of step size according to this principle is applied to the SE-MMA and DSE-MMA, namely mSE-MMA and mDSE-MMA algorithm, the algorithm's performance were compared in the same channel and noise environment by computer simulation. As a result of simulation, the mSE-MMA has more superior to the mDSE-MMA in residual value of every performance index and SER performance, and the vice versa result in convergence speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.101-106
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• 2019

This paper propose the VSS-FC-MMA algorithm that is possible to improve the equalization performance based on varying step size to the FC-MMA adaptive equalization algorithm in order to reducing the intersymbol interference effect occurred in the nonconstant modulus signal transmission, and improved performance were confirmed. The FC-MMA is possible to improve the convergence speed, and degrades the steady state performance based on the fixed step size and modified dispersion constant considering the level number of signal symbol for obtain the error signal in adaptive equalization compared to MMA. The proposed VSS-FC-MMA uses varying step size and current FC-MMA possible to improve the steady state equalization performance, it was confirmed by computer simulation. For this, the signal recovery capabilities and residual isi, MSE, SER were applied for performance comparison index in the same channel and signal to noise ratio. As a result of computer simulation, the proposed VSS-FC-MMA improve the risidual value in steady state and SER performance than the FC-MMA, but has 1.7 times slow convergence time by using varying step size.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.45-50
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• 2022

This paper compares the equalization performance of the DSE-MMA (Dithered Signed Error-MMA) and DQE-MMA (Dithered Quantized Error-MMA) adaptive equalization algorithm based on the dither signal in order to reduce the intersymbol interference occurs at communication channel. These algorithm was emerged in ordr to reduction of arithmetic operation than current MMA, it makes the independent and identical distribute the quantized error component by performing the 1 or N bit quautizer after adding the dither singal in obtaining the error signal for adapting process. It is possible to improve the robustness performance of adaptive algorithm, but degrade the MSE performance in steady state by dither signal. The paper directly compare the DSE-MMA and DQE-MMA adaptive equalization performance of the same concept of dithering in the same communication channel and signal to noise ratio by computer simulation. As a result of simulation, the DQE-MMA has more better in the every performance index, equalizer output constellation, residual isi, MSE and SER performance, but not in convergence speed.

• Journal of Advanced Navigation Technology
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• v.20 no.2
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• pp.161-166
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• 2016

In high capacity and long haul optical communication systems, signal distortion is induced by chromatic dispersion and nonlinear effects of optical fibers. Dispersion management (DM) combining with mid-spans spectral inversion (MSSI) is one of the various techniques for overcoming this drawback. The most simple configuration of DM link is obtained by uniformly distributing the lengths of single mode fiber (SMF) and residual dispersion per span (RDPS) over whole fiber spans consisted of optical link. In this paper, the system performances in the uniformly distributed DM link combined with MSSI are assessed as a function of the number of fiber spans, because the system performances in this configuration are used as the significant performance criterion in other link configurations, such as the artificial distribution or the random distribution of SMF lengths and RDPS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2480-2496
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• 2020

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.961-973
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• 2020

In this manuscript, a new pansharpening model based on Convolutional Neural Network (CNN) was developed. Dilated convolution, which is one of the representative convolution technologies in CNN, was applied to the model by making it deep and complex to improve the performance of the deep learning architecture. Based on the dilated convolution, the residual network is used to enhance the efficiency of training process. In addition, we consider the spatial correlation coefficient in the loss function with traditional L1 norm. We experimented with Dilated Residual Networks (DRNet), which is applied to the structure using only a panchromatic (PAN) image and using both a PAN and multispectral (MS) image. In the experiments using KOMPSAT-3A, DRNet using both a PAN and MS image tended to overfit the spectral characteristics, and DRNet using only a PAN image showed a spatial resolution improvement over existing CNN-based models.