• Journal of Communications and Networks
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• v.5 no.2
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• pp.157-166
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• 2003

This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.87-92
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• 2012

Speech enhancement aims to improve speech quality by removing background noise from noisy speech. Independent vector analysis is a type of frequency-domain independent component analysis method that is known to be free from the frequency bin permutation problem in the process of blind source separation from multi-channel inputs. This paper proposed a new method of microphone array based speech enhancement that combines independent vector analysis and beamforming techniques. Independent vector analysis is used to separate speech and noise components from multi-channel noisy speech, and delay-sum beamforming is used to determine the enhanced speech among the separated signals. To verify the effectiveness of the proposed method, experiments for computer simulated multi-channel noisy speech with various signal-to-noise ratios were carried out, and both PESQ and output signal-to-noise ratio were obtained as objective speech quality measures. Experimental results have shown that the proposed method is superior to the conventional microphone array based noise removal approach like GSC beamforming in the speech enhancement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.49-55
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• 2008

To address the convergence issue of power control algorithms, a number of algorithms have been developed hat shape the dynamics of up-link power control for cellular network. Power algorithms based on fixed point iterations can be accelerated by the use of various methods, one of the simplest being the use of Newton iterations, however, this method has the disadvantage which not only needs derivatives of the cost function but also may be weak to noisy environment. we showed performance of the power control schemes to solve the fixed point problem under static or stationary channel. They proved goof performance to solve the fixed point problem due to their predictor based optimal control and quadratic convergence rate. Here, we apply the proposed power control schemes to the problem of the dynamic channel or to dynamic time varying link gains. The rigorous simulation results demonstrated the validity of our approach.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.41-48
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• 1997

This paper proposes the methods for improving the recognition rate of theARS, especially equipped with the speech recognition capability. Telephone speech, which is the input to the ARS, is usually affected by the announcements from the system, channel noise, and channel distortion, thus directly applying the recognition algorithm developed for clean speech to the noisy telephone speech will bring the significant performance degradation. To cope with this problem, this paper proposes three methods: 1)the accurate detection of the inputting instant of the speech in order to immediately turn off the announcements from the system at that instant, 2)the effective end-point detection of the noisy telephone speech on the basis of Teager energy, and 3)the SDCN-based compensation of the channel distortion. Experiments on speaker-independent, noisy telephone speech reveal that the combination of the above three proposed methods provides great improvements on the recognition rate over the conventional method, showing about 77% in contrast to only 23%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.397-402
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• 2009

Accurate estimation of time-selective fading channel is a difficult problem in OFDM(Orthogonal Frequency Division Multiplexing) system. There are many channel estimation algorithms that are very weak in noisy channel. For solving this problem, we use EM (Expectation-Maximization) algorithm for iterative optimization of the data. We propose an EM-LPC algorithm to estimate the time-selective fading. The proposed algorithm improves of the BER performance compared to EM based channel estimation algorithm and reduces the iteration number of the EM loop. We simulated the uncoded system. If coded system use the EM-LPC algorithm, the performance are enhanced because of the coding gain. The EM-LPC algorithm is able to apply to another communication system, not only OFDM systems. The image processing of the medical instruments that the demand of accurate estimation can also use the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.85-88
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• 2001

In this paper we consider a problem of blind co-channel signal separation, the goal of which is to estimate multiple co-channel digitally modulated signals using an antenna array. We employ the joint maximum likelihood estimation and present a sequential algorithm, which is referred to as sequential joint maximum likelihood (SJML) algorithm. It separates multiple co-channel signal on-line and converges fast in overdetermined noisy communication environment. And the computational complexity of SJML for M-QAM (M=8, 16, 64,...) signals is less expensive compared to the SLSP. Useful behavior of this algorithm are confirmed by simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.58-58
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• 2002

Recently Many technologies of the P.L.N (Power Line Network) are introduced for an efficient communication. P.L.N transmits information using power line channel. A medical information transmission system is proposed in this paper for the effective transmission. The Power Line Networks have an advantage in low cost because it uses existent power line. It can be easily applied anywhere that a power line socket is available. But the power line channel is very noisy, because many electronic devices in generate electronic noise in modulated signals. This problem should be considered carefully in the design of medical information transmission system using P.L.N. The proposed system can be applied...I propose a telemedicine system using the power line network.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1043-1048
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• 2011

In this paper, a newsynchronization technique applied to burst-mode communication is proposed. A synchronization technique is to estimate carrier frequency and phase offsets in a noisy channel environment. A fundamental problem for estimating the parameters(carrier phase and frequency offsets) in burst-mode transmission is that the ways of pursuing estimation accuracy and transmission efficiency are always trade-off. To solve this problem, a new carrier recovery technique is proposed to improve the transmission efficiency with reliable performance especially at low S/N. In the proposed technique, the synchronization parameters are first estimated based on a data-aided feed-forward estimation scheme. Then, a phase tracker using decision-directed DPLL estimates the phase offset for the data portion of the burst data. From simulation results, it shows fast synchronization with shorter preamble maintaining reasonable BER performance at low S/N.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.285-297
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• 2015

In this paper, Ulta-WideBand (UWB) channel measurement and modeling based on compressive sampling (CS) are proposed. The sparsity of the channel impulse response (CIR) of the UWB signal in frequency domain enables the proposed channel measurement to have a low-complexity and to provide a comparable performance compared with the existing approaches especially used for the indoor geo-localization purpose. Furthermore, to improve the performance under noisy situation, the soft thresholding method is also investigated in solving the optimization problem for signal recovery of CS. Via numerical results, the proposed channel measurement and modeling are evaluated with the real measured data in terms of location estimation error, bandwidth, and compression ratio for indoor geo-localization using UWB system.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.468-474
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• 2010

The major factor that disturbs practical use of speech recognition is distortion by the ambient and channel noises. Generally, the ambient noise drops the performance and restricts places to use. DSR (Distributed Speech Recognition) based speech recognition also has this problem. Various noise cancelling algorithms are applied to solve this problem, but loss of spectrum and remaining noise by incorrect noise estimation at low SNR environments cause drop of recognition rate. This paper proposes methods for speech enhancement. This method uses MMSE-STSA for noise cancelling and ideal binary mask to compensate damaged spectrum. According to experiments at noisy environment (SNR 15 dB ~ 0 dB), the proposed methods showed better spectral results and recognition performance.