• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.943-951
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• 2012

In this paper, we propose a novel spectrum sensing scheme based on the order statistic for cooperative cognitive radio network in non-Gaussian noise environments. Specifically, we model the ambient noise as the bivariate isotropic symmetric ${\alpha}$-stable random variable, and then, propose a cooperative spectrum sensing scheme based on the order of observations and the generalized likelihood ratio test. From numerical results, it is confirmed that the proposed scheme offers a substantial performance improvement over the conventional scheme in non-Gaussian noise environments.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.37-44
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• 2020

The fifth generation (5G) mobile communication has been commercialized and the 5G applications, such as the artificial intelligence (AI) and the internet of things (IoT), are deployed all over the world. The 5G new radio (NR) wireless networks are characterized by 100 times more traffic, 1000 times higher system capacity, and 1 ms latency. One of the promising 5G technologies is non-orthogonal multiple access (NOMA). In order for the NOMA performance to be improved, sometimes the additive signal-dependent Gaussian noise (ASDGN) channel model is required. However, the channel capacity calculation of such channels is so difficult, that only lower and upper bounds on the capacity of ASDGN channels have been presented. Such difficulties are due to the specific constraints on the dependency. Herein, we provide the capacity of ASDGN channels, by removing the constraints except the dependency. Then we obtain the ASDGN channel capacity, not lower and upper bounds, so that the clear impact of ASDGN can be clarified, compared to additive white Gaussian noise (AWGN). It is shown that the ASDGN channel capacity is greater than the AWGN channel capacity, for the high signal-to-noise ratio (SNR). We also apply the analytical results to the NOMA scheme to verify the superiority of ASDGN channels.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.418-422
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• 2013

This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.383-391
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• 2009

In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• ETRI Journal
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• v.32 no.6
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• pp.837-842
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• 2010

This paper proposes robust source localization methods for estimating the azimuth angle, elevation angle, velocity, and range using a low-complexity planar antenna array in impulsive non-Gaussian noise environments. The proposed robust source localization methods for wireless communication applications are based on nonlinear M-estimation provided from Huber and Hampel. Simulation results show the robustness performance of the proposed robust methods in impulsive non-Gaussian noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.3
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• pp.135-146
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• 1985

The error rate equation of MSK(Minimum Shift Keying) signal transmitted through the fading channel has been derived in the interference and impulsive noise environments. The error rate performance of MSK system have been evaluated and shown in figures as parameters of carrier-to-noise power ratio(CNR), carrier-to-interferer power ratio(CIR), impulsive index, the ratio of Gaussian noise power component to impulsive noise power component, and fading figure. The results show that, in the fading environment, the error is occurred more frequently by Gaussian noise in the deep fading, however in the shallow fading, impulsive noise is more dominant than Gaussian noise. And in thepresence of interference and fading, interferer deteriorates the MSK receiving system severely even if the interferer is weak.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.1-6
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• 2016

The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.