• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2240-2247
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• 1998

This paper presents a new method for channel impulse response measurment with a suitably designed binary sounding sequence. This method results in ideal channel estimation using three correlators instead of one. This system complexity can be easily overcome by the present technologies. The class of sounding sequences used in this method are the training sequence for channel impulse response measurment with the zero values of autocorrelation function at all shifts except zero and middle shifts in digital cellular mobile telephone system based on GSM. Computer searches are carried out to find the suitable sounding sequences.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.603-611
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• 2003

This paper proposes an effective feature compensation scheme based on speech model for achieving robust speech recognition. Conventional model-based method requires off-line training with noisy speech database and is not suitable for online adaptation. In the proposed scheme, we can relax the off-line training with noisy speech database by employing the parallel model combination technique for estimation of correction factors. Applying the model combination process over to the mixture model alone as opposed to entire HMM makes the online model combination possible. Exploiting the availability of noise model from off-line sources, we accomplish the online adaptation via MAP (Maximum A Posteriori) estimation. In addition, the online channel estimation procedure is induced within the proposed framework. For more efficient implementation, we propose a selective model combination which leads to reduction or the computational complexities. The representative experimental results indicate that the suggested algorithm is effective in realizing robust speech recognition under the combined adverse conditions of additive background noise and channel distortion.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.996-1004
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• 2004

1-Dimensional Cluster-Based Sequence Equalizer(1-D CBSE) lessens computational load, compared with the classic maximum likelihood sequence estimation(MLSE) equalizers, and has the superiority in the nonlinear channels. In this paper, we proposed an algorithm of searching for optimal training sequence that estimates the cluster centers instead of time-varying multipath fading channel estimation. The proposed equalizer not only resolved the problems in 1-D CBSE but also improved the bandwidth efficiency using the shorten length of taming sequence to improve bandwidth efficiency. In experiments, the superiority of the new method is demonstrated by comparing conventional 1-D CBSE and related analysis.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.8-12
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• 2007

In this paper, we proposed an frequency offset estimation scheme which can be used for packet based OFDM communication systems such as IEEE802.11a and IEEE802.11p physical layer. Proposed estimation scheme can adjust the gain allocation ratio between long training symbol and short training symbol while maintaining average power of overall training sequence so that we can obtain the reference parameters for MSE performance improvement. The preamble structure considered in this paper is based on the preamble specified in IEEE802.11a and IEEE802.11p standardization group. From the simulation results, it is shown that power ratio between long training symbol and short training symbol must vanes for achieving lower frequency offset estimation error as channel SNR condition is changed. Also it is known oat proposed scheme can achieve better performance than conventional one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.753-762
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• 2002

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1277-1282
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• 2016

In the next generation wireless communication systems, an energy harvesting from radio frequency signals is considered as a method to solve the lack of power supply problem for sensors. In this paper, we try to propose an efficient algorithm for simultaneous wireless information and power transfer in energy harvesting networks with channel estimation error. At first, we find an optimal channel training interval using one-dimensional exhaustive search, and estimate a channel using MMSE channel estimator. Based on the estimated channel, we propose a power allocation and splitting algorithm for maximizing the data rate while guaranteeing the minimum required harvested energy constraint, The simulation results confirm that the proposed algorithm has an insignificant performance degradation less than 10%, compared with the optimal scheme which assumes a perfect channel estimation, but it can improve the data rate by more than 20%, compared to the conventional scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.142-149
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• 2013

In general, a color of an image is represented by using red, green, and blue channels in a RGB camera system. However, only information of three channels are limited to estimate a spectral reflectance of a real scene. Because of this, the RGB camera system can not accurately represent the color. To overcome this limitation and represent an accurate color, researches to estimate the spectral reflectance by using a multi-channel camera system are being actively proceeded. Recently, a reflectance estimation method adaptively constructing a similar training set from a traditional training set according to a camera response by using a spectral similarity was introduced. However, in this method, an accuracy of the similar training set is reduced because the spectral similarity based on an average and a maximum distances was applied. In this paper, a reflectance estimation method applied a spectral similarity based on a correlation coefficient is proposed to improve the accuracy of the similar training set. Firstly, the correlation coefficient between the similar training set and the spectral reflectance obtained by Wiener estimation method is calculated. Secondly, the similar training set is constructed from the traditional training set according to the correlation coefficient. Finally, Wiener estimation method applied the similar training set is performed to estimate the spectral reflectance. To evaluate a performance of the proposed method with previous methods, experimental results are compared. As a result, the proposed method showed the best performance.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.378-384
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• 2015

Indoor localization is becoming one of the most important technologies for smart mobile applications with different requirements from conventional outdoor location estimation algorithms. Fingerprinting location estimation techniques based on neural networks have gained increasing attention from academia due to their good generalization properties. In this paper, we propose a novel location estimation algorithm based on an ensemble of multiple neural networks. The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its' inaccuracy, incompleteness, and ambiguity. To the best of our knowledge, this work is the first to enhance the location estimation accuracy in indoor wireless environments based on a neural network ensemble using fingerprinting training data. To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high capacity WLAN technologies in indoor environments with multiple transmit and multiple receive antennas. The numerical results show that the proposed method based on the NNE technique outperforms the conventional methods and achieves very accurate estimation results even in environments with a low number of APs.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.552-559
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• 2005

Recurrent neural networks(RNNs) trained with gradient based such as real time recurrent learning(RTRL) has a drawback of slor convergence rate. This algorithm also needs the derivative calculation which is not trivialized in error back propagation process. In this paper a derivative free Kalman filter, so called the unscented Kalman filter(UKF), for training a fully connected RNN is presented in a state space formulation of the system. A derivative free Kalman filler learning algorithm makes the RNN have fast convergence speed and good tracking performance without the derivative computation. Through experiments of nonlinear channel equalization, performance of the RNNs with a derivative free Kalman filter teaming algorithm is evaluated.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1693-1696
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• 2002

In this paper, we analyzed the performance of OFDM system based on IEEE 802.11a specification. First, we modeled the transmitter and receiver of OFDM (Orthogonal Frequency Division Multiplexing) system. Then, we analyzed the performance of OFDM system through simulation over the JTC (Joint Technical Committee) realistic channel model. In addition we carried out the performance by using pilot training symbol, which is one of the channel estimation methods, over the same channel environments.