• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.9
• /
• pp.798-809
• /
• 2013

In this paper, we propose robust relay and destination filter design methods for the multi-user peer-to-peer amplify-and-forward relaying systems while taking imperfect channel knowledge into consideration. Specifically, the relay and destination filter sets are developed to minimize the sum mean-squared-error (MSE). We first present a robust joint optimum relay and destination filter calculation method with an iterative algorithm. Motivated by the need to reduce computational complexity of the iterative scheme, we then formulate a simplified sum MSE minimization problem using the relay filter decomposability, which lead to two robust sub-optimum non-iterative design methods. Finally, we propose robust modified destination filter design methods which require only local channel state information between relay node and a specific destination node. The analysis and simulation results verify that, compared with the optimum iterative method, the proposed non-iterative schemes suffer a marginal loss in performance while enjoying significantly improved implementation efficiencies. Also it is confirmed that the proposed robust filter design methods provide desired robustness in the presence of channel uncertainty.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.5
• /
• pp.490-496
• /
• 2020

We present a method to improve the performance of the multi-channel Wiener filter in noisy environment. To build subspace-based multi-channel Wiener filter, in the case of single target source, the target speech component can be effectively estimated in the principal subspace of speech correlation matrix. The speech correlation matrix can be estimated by subtracting noise correlation matrix from signal correlation matrix based on the assumption that the cross-correlation between speech and interfering noise is negligible compared with speech correlation. However, this assumption is not valid in the presence of strong interfering noise and significant error can be induced in the principal subspace accordingly. In this paper, we propose to adjust the principal subspace vector using speech presence probability and the steering vector for the desired speech source. The multi-channel speech presence probability is derived in the principal subspace and applied to adjust the principal subspace vector. Simulation results show that the proposed method improves the performance of multi-channel Wiener filter in noisy environment.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.49 no.5
• /
• pp.51-58
• /
• 2012

Accurate transmission and estimation of the channel statistics affected by high Doppler spread is one of the main issues of concern for the latest and future mobile communication systems. Therefore, it is important to research in novel channel estimation techniques that overcome the limitations of conventional methods. In this paper, we propose a novel channel estimation method that, after a simple estimation in the first OFDM symbol, uses Kalman filter to predict the channel in the rest of OFDM symbols with pilot subcarriers. Our method is designed considering the lattice-type arrangement of pilot subcarriers in LTE-Advanced, since most of the studies so far focus on block-type or comb-type pilot arrangements. In addition, to optimize the results, we use the filtering of channel impulse response and Wiener Filter for the estimation of the channel frequency response in the rest of the subcarriers.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.887-892
• /
• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Speech Sciences
• /
• v.11 no.1
• /
• pp.43-52
• /
• 2004

This paper concerns an effective dual-channel noise reduction method to increase the performance of speech recognition in a car environment. While various single channel methods have already been developed and dual-channel methods have been studied somewhat, their effectiveness in real environments, such as in cars, has not yet been formally proven in terms of achieving acceptable performance level. Our aim is to remedy the low performance of the single and dual-channel noise reduction methods. This paper proposes an effective dual-channel noise reduction method based on a high-pass filter and front-end processing of the eigendecomposition method. We experimented with a real multi-channel car database and compared the results with respect to the microphones arrangements. From the analysis and results, we show that the enhanced eigendecomposition method combined with high-pass filter indeed significantly improve the speech recognition performance under a dual-channel environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.11
• /
• pp.917-924
• /
• 2003

Recurrent neural networks have been successfully applied to communications channel equalization. Major disadvantages of gradient-based learning algorithms commonly employed to train recurrent neural networks are slow convergence rates and long training sequences required for satisfactory performance. In a high-speed communications system, fast convergence speed and short training symbols are essential. We propose decision feedback equalizers using a recurrent neural network trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, utilizing extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence rates and good performance using relatively short training symbols. Experimental results for two time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer.

• Journal of information and communication convergence engineering
• /
• v.2 no.4
• /
• pp.205-208
• /
• 2004

In this paper, we analyzed the performance of SDR-based dual-band CDMA system in wideband multipath channel employing RAKE receiver with MRC diversity. For the simulation of SDR-based dual-band CDMA system, we used digital IF techniques, polyphase analysis filter bank as channelizer. Also, Remez exchange algorithm is employed in the realization of the digital filter in the polyphase analysis filter bank. For performance analysis of dual-band CDMA system, we consider power loss of IF signal after passing through channel and the filter. Also, the performance for different sampling rates is analyzed.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.9
• /
• pp.1444-1452
• /
• 1989

This paper describes the implementation of a real time fetal ECG monitoring system in which an adaptive multi-channel noise canceller is realized using the Texas Instruments TMS32020 progrmmmable ditital signal processor. An ECG signal from the electrode placed on the mother's abdomen and three ECGs from those on the chest are applied as the desired signal and the referened inputs, respectively, of the multi-channel filter. The coefficients of the filter are updated using the LMS algorithm such that the output of the multi-channel filter copies the maternal ECG embedded in the abdominal ECG. The enhanced fetal ECG is obtained by subtracting the filter output from the abdominal ECG, and the difference signal is recorded. Both off-line and on-line experimental results are presented to verify the effectiveness of the parameters for the digital signal processing algorithms and the prototype system.

• ETRI Journal
• /
• v.31 no.2
• /
• pp.231-233
• /
• 2009

We propose a nonreciprocal filter based on coarse wavelength division multiplexing (CWDM) that reduces the upstream channel insertion loss in a passive optical network (PON). We also propose a method to increase the number of channels/optical network units (ONUs) in PON systems using the proposed filter to reduce the service cost per subscriber. Experimental results show that the PON system with the proposed 4-channel filter can reduce the power budget of the upstream and increase the number of ONUs by 3 to 4 times that of a conventional time-division multiplexing PON.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2003.11a
• /
• pp.127-131
• /
• 2003

In this paper an adaptive channel estimator is proposed and investigated which improves the receiving performance for the DVB-T system. A conventional estimator for the system consists of a two-dimensional Wiener filter which is implemented as a cascade of one-dimensional filters, and the filter is operating with the filter coefficients set which is selected from the four different sets according to the channel environment. Our proposed estimator uses the filter coefficients which is interpolated by the two closest coefficients sets. The proposed scheme shows an improvement of 5 to 10dB in SNR compared to the conventional scheme.