• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.194-198
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• 2011

In this paper, we obtained the path loss characteristics in the 850 MHz for Russia area by using the free space path loss model and Okumura-Hata path loss model. In order to extract the additional path loss model parameter from the new Russian regional properties, the mean square error technique is used to obtain the correction factor. According to the obtained correction factor, the differences for the free space and Hata path loss model are 17, 6 dB in the 5 ~ 10 Km, 28, 14 dB in the 10 ~ 15 Km, and 35, 18 dB in the 15 ~ 20 Km. By applying the correction factors, the appropriate partial path loss models for the measured Russain area are proposed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.88-94
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• 2013

In this paper, an efficient signal interference control technique to improve the convergence speed of LMS algorithm is introduced. The convergence characteristics of the proposed algorithm,whose coefficients are multiply adapted in a symbol time period by recycling the received data,are analyzed to prove theoretically the improvement of convergence speed. According as thestep-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the average squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.188-194
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• 2000

이동 통신에서 제한된 대역폭 채널에 내부 심볼 간섭을 감소시키기 위해, 등화기 기법을 필요로한다. 채널간의 비선형 왜곡을 효율적으로 다루는 대안을 가진 신경망을 사용하여 새로운 활성 함수로 구성된 적응 역전파 알고리즘을 연구한다. 신경망은 적응 역전파 알고리즘을 통해 신호를 복조하도록 학습한다. 특히 수정된 적응 역전파 알고리즘이 근접된 최적 수행성을 갖는 단일 및 다중 사용자 검출을 위한 샘플링 기법은 다중 사용자 환경에서 필요한 수신기들의 수행성을 평가하기 위한 시뮬레이션을 위하여 사용이 된다. 채널간의 비선형 왜곡에 효율적으로 다루기 위한 대안을 가진 신경망을 적용하여 본 논문에서 는 새로운 활성 함수로 구성된 적응 역전파 알고리즘을 제안하고, 컴퓨터 시뮬레이션에 의해서 분석된다. 반복적 최소 평균 자승(RLS) 알고리즘을 적용한 기존 수신기 및 적응 역전파 신경망과 비교하여, 채널 왜곡이 비선형 일 때에 비트 에러율(BER)이 현저하게 개선됨을 나타낸다. 적응 역전파 알고리즘 기법을 통해 기존 수신기와 신경망을 사용한 수신기의 수행을 컴퓨터 시뮬레이션을 통해 비교 분석하여 제안된 신경망 수신기의 성능이 우수함을 인증한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.245-249
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• 2011

In this paper, we propose an equivalent Wiener-Hopf equation. The proposed algorithm can obtain the weight vector of a TDL(tapped-delay-line) filter and the error simultaneously if the inputs are orthogonal to each other. The equivalent Wiener-Hopf equation was analyzed theoretically based on the MMSE(minimum mean square error) method. The results present that the proposed algorithm is equivalent to original Wiener-Hopf equation. In conclusion, our method can find the coefficient of the TDL (tapped-delay-line) filter where a lattice filter is used, and also when the process of Gram-Schmidt orthogonalization is used. Furthermore, a new cost function is suggested which may facilitate research in the adaptive signal processing area.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.520-525
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• 2009

The conventional studies about an adaptive beamformer assumed that the interference signals are stationary, so they used time-average of signals or Least Mean Squares. However, these methods showed low performance of canceling the non-stationary interferences. In this paper, the MAFF-RLS algorithm is developed in order to cancel non-stationary interferences, and the GSC structure using this algorithm is proposed. Furthermore, the performance of the MAFF-RLS beamformer is verified by simulation using MATLAB. This simulation results show the performance of the proposed beamformer is better than that of the SMI and the conventional RLS beamformer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.328-336
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• 2006

A novel pre-equalization method for terrestrial DTV transmitters is presented. A pre-equalizer has been used in transmitters to correct group delay and amplitude distortions caused by a channel filter. In the proposed pre-equalizer, an equation-error adaptive IIR filtering scheme is adopted unlike the conventional pre-equalization using FIR filtering schemes. The pole-zero modelling property of IIR filters improves the signal-to-noise ratio and may deal with diverse linear distortions existing in DTV transmitters as well as the channel filter distortion. Simulation results show that the proposed IIR pre-equalizer performs better than the FIR pre-equalizer in terms of the residual mean-square error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.269-274
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• 2004

In this paper, proposed is the dual-mode algorithm of blind decision feedback equalizer (DFE) for digital terrestrial television signals. According to channel impairments, the proposed dual-mode algorithm for blind DFE operates in decision-directed mode or in blind mode of operation. The error signals being used in tap update of the equalizer are generated in the best mode of operations, so that the confidence of equalizer tap coefficient update is more accurate. As a result, it is possible to track the channel characteristics variations by automatic switching over between two modes of operations. For 8-level vestigial sideband modulated digital television signals, the mean square errors and symbol error rates of the proposed algorithm are compared with those of conventional methods. And the usability of the proposed scheme is assessed by computer simulations under various static and dynamic multipath channel environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.987-994
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• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.8-15
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• 2020

In this paper, we propose a frame synchronization algorithm for robust to the combined effects of large Doppler fluctuations and extended, time-varying multipath in the underwater acoustic communication. From the algorithm, we can recover a high timing error which is occurred from an acoustic propagation delay and uncertainty of oscillator between transmitter and receiver. In order to verify the performance of the synchronization algorithm, the lake trial results are used. The lake experiments are performed in a Gyeongcheonho located in Mungyeong-si, Gyeongsangbuk-do. We can see that the start position of frame is adjusted after the frame synchronization while the receiver moving.