• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.9
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• pp.995-1004
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• 2010

In the channel-varying environment, it is very important to estimate the signal to noise ratio(SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary APSK or QAM. In this paper, we propose a SNR estimation method which uses zero point auto-correlation of received signal per block and auto-/cross- correlation of decision feedback signal in OFDM system. Proposed method can be studied into two Types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the previous SNR estimation methods. Also, we mathematically derive the SNR estimation expression according to computational difference of auto-/cross-correlation. Finally, Monte Carlo simulations are used to verify the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.383-391
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• 2009

In speech signal processing, speech signal corrupted by noise should be enhanced to improve quality. Usually noise estimation methods need flexibility for variable environment. Noise profile is renewed on silence region to avoid effects of speech properties. So we have to preprocess finding voice region before noise estimation. However, if received signal does not have silence region, we cannot apply that method. In this paper, we proposed SNR estimation method for continuous speech signal. The waveform which is stationary region of voiced speech is very correlated by pitch period. So we can estimate the SNR by correlation of near waveform after dividing a frame for each pitch. For unvoiced speech signal, vocal track characteristic is reflected by noise, so we can estimate SNR by using spectral distance between spectrum of received signal and estimated vocal track. Lastly, energy of speech signal is mostly distributed on voiced region, so we can estimate SNR by the ratio of voiced region energy to unvoiced.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.3
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• pp.282-291
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• 2011

Adaptive modulation(AM) is an important technique to increase the system efficiency, in which transmitter selects the most suitable modulation mode adaptively according to channel state in the temporary and spatially varying communication environment. Fixed modulation on channels with varying signal-to-noise ratio(SNR) is that the bit-errorrate(BER) probability performance is changing with the channel quality. An adaptive modulation scheme can be designed to have a BER which is constant for all channel SNRs. The correct as well as fast and simple SNR estimation is required essentially for this adaptive modulation. In order to operate adaptive modulation system effectively, in this paper, we analyze the effect of SNR estimation performance to it through the average BER and data throughput. Applying SNR estimation based on auto-correlation of decision feedback signal and others to adaptive modulation system, we also confirm performance degradation or improvement of its which is decided by SNR estimation error at each transition point of modulation level. Since SNR estimation based on auto-correlation of decision feedback signal shows stable estimation performance for various quadrature amplitude modulation(QAM) comparatively, this can be reduced degradation than others at each transition point of modulation level.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.148-153
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• 2010

In speech signal processing, speech signal corrupted by noise should be enhanced to improve quality. Usually noise estimation methods need flexibility for variable environment. Noise profile is renewed on silence region to avoid effects of speech properties. So we have to preprocess finding voice region before noise estimation. However, if received signal does not have silence region, we cannot apply that method. In this paper, we proposed SNR estimation method for continuous speech signal. A Speech signal consists of Voice and Unvoiced Band in The MBE excitation model. And the energy of speech signal is mostly distributed on voiced region, so we can estimate SNR by the ratio of voiced region energy to unvoiced. We use the IMBE vocoder for the Voice or Unvoice band of segmented speech signal. Continuously we calculate the segmented SNR using that information and the energy of each band. And we estimate the SNR of continuous speech signal.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.927-934
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• 2013

This paper aims to realize a Signal to Noise Ratio Estimator which constitutes a critical index of the broadcasting system in OFDM system with a synchronized type based on ISDB-T system. Of the elements which are comprising OFDM segments of ISDB-T system using the MSE algorithm suitable for ASIC design owing to its low complexity among a diverse SNR estimation methods, SNR estimation method using the broadcasting information data and the SNR estimation method using scattered pilot signal were realized by RTL. These two methods were compared in terms of their performance through simulation test not only in the AWGN channel which is an ideal channel, but also in SFN channel and frequency selective fading channel, which are distorted channels. Complexity of two methods were also compared through RTL realization. As a result of this comparison analysis, it was concluded that the SNR estimation method using scattered pilot signal shows more excellent performance and easiness in realization.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.391-394
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• 1998

본 논문에서는 극심한 가산 잡음에 의해 손상된 음성 신호를 스펙트럼 차감법으로 개선할 때, 잡음 스펙트럼 추정을 위한 무음 구간 추정 방법을 제안한다. 스펙트럼 차감법은 잡음을 효과적으로 제거한다고 알려져 있으나, SNR 0 dB 이하의 잡음 환경에서는 무음 구간의 검출이 힘들어 잡음 스펙트럼 추정치의 정확도가 저하된다. 일반화 스펙트럼 차감법의 과차감(oversubtraction)과 잡음 스펙트럼 추정을 반복하여 얻은 무음 구간은 SNR -10 dB~ 0 dB의 낮은 SNR에서도 비교적 정확하며, 프레임 에너지를 이용한 무음 검출 방법에 비해 향상된 성능을 보였다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.112-117
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• 1999

In case of spectral subtraction for noise robust speech recognition system, this method often makes loss of speech signal. In this study, we propose a method that variation and determination of Gaussian function at semi-continuous HMM(Hidden Markov Model) is made on the basis of SNR criteria function, in which SNR means signal to noise ratio between estimation noise and subtracted signal per frame. For proving effectiveness of this method, we show the estimation error to be related with the magnitude of estimated noise through signal waveform. For this reason, Gaussian function is varied and determined by SNR. When we test recognition rate by computer simulation under the noise environment of driving car over the speed of 80㎞/h, the proposed Gaussian decision method by SNR turns out to get more improved recognition rate compared with the frequency subtracted and non-subtracted cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8A
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• pp.671-678
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• 2005

This paper proposed symbol rate method which does not require a priori knowledge on the symbol rate and simplified modulation identification method to classify BPSK, QPSK, 8PSK signal. In order to estimate the unknown symbol rate, sliding FFT and simple moving average to estimate the spectrum of the signals is utilized, and sliding window and decimation, LPF blcok to estimate the proper symbol rate is used. Although conventional modulation ID method must use SNR value as the test statistics, the receiver cannot estimate the SNR value since the receiver cannot know the modulation type at the start of communication, and bit resolution is high due to using nonlinear function such as log, cosh. Therefore, we proposed the simplified fixed SNR value method. The performance of symbol rate estimation and modulation ID is shown using Monte Carlo computer simulation. This paper show that symbol rate estimation also has good performance in low SNR, and proposed simplified fixed SNR method has almost equivalent performance compared to conventional method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.139-145
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• 2010

In this paper, we propose robust cell ID detection algorithm and SNR estimation algorithm applicable to mobile base station, which can be operated independently. The proposed cell ID estimation uses signal subspace to estimate cell IDs used in cell. The proposed SNR estimation algorithm uses number of noise subspace vectors and the corresponding eigen-vectors. Through the computer simulations, we showed that performance of the proposed cell ID detection and SNR estimation algorithms are superior to existing correlation based algorithms. Also we showed that the proposed algorithm is suitable to fast moving channel in high background noise and strong interference signal.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.32-37
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• 1991

고해상도 신호처리의 기본적인 문제는, 관찰 데이터의 개수가 작고 신호 대 잡음비(SNR)가 낮아서, 푸리에 분석기법에 의해 주기신호가 분해되지 않는 경우에, 신호의 파라미터를 추정하는 것이라 할 수 있다. 주기신호의 주파수 추정 문제에서는 일반적으로 주기신호의 개수를 알고 있다고 가정하는데, 주기신호의 개수가 사전에 알려져 있지 않은 경우, 주파수 추정은 결국 주기신호의 개수결정문제가 되어, EVD나 SVD를 이용한 개수 결정방법이 활발히 연구되어 왔다. 고해상도 신호처리에서는 EVD나 SVD의 비선형 특성 상임게치 신호 대 잡음비가 존재하며 이 SNR보다 낮은 경우 심각한 왜곡현상을 보이게 되어, 주파수 추정 또는 주기신호의 개수결정에 큰 오차를 보이게 된다. 주기신호의 개수를 사전에 알고 있는 경우, 임게치 SNR를 낮추려는 노력으로는 overdetermined over-ranked structured correlation matrix의 rank reduction과 averaging을 이용한 신호 향상방법(signal enhancement)이 연구되어 왔다. 그러나 사전에 주기신호의 개수를 알아야만 하는 결점이 있고, 잡음이 백색이여야 하는 제약이 있었다. 일반적으로 환경 잡음은 유색이고, 주기신호의 개수를 사전에 모르는 경우이므로, 낮은 SNR에서의 주파수 추정문제는 유색잡음을 고려한 신호향상으로 임게치 SNR을 낮추고 주기신호의 개수를 결정한 후 주파수 추정이 이루어져야 한다. 본 논문에서는 이를 위해 광대협 유색잡음에서의 신호향상과 그 과정 중 중 주기신호의 개수를 결정하는 알고리즘ㅇ르 제시하고자 한다.