• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.982-987
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• 2012

In this paper, the SNR estimation error of Decision Directed SNR estimation method in AWGN is investigated, which uses samples received in reference decision region. In communication system receiver, when SNR estimation scheme using error vectors between ideal sample points and received sample points of reference region is adopted, the samples contain incorrectly received samples due to AWGN. Consequently, the mean of estimated reference constellation point is shifted and Decision Directed SNR estimation is inaccurately performed. These effects are explained by modified probability density function and difference between actual SNR and estimated SNR is theoretically derived and quantatively analyzed. It is proved that SNR estimation error obtained through computer simulation is matched up with derived one, and SNR estimation performance is enhanced significantly by adopting suggested correction scheme.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.975-981
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• 2012

The SNR estimation performance of hierarchical 16QAM system, which is adopted to simultaneous transmission or efficient image transmission system, is analyzed. Hierarchical 16QAM is modulation system which has different constellation shape from conventional QAM and can provide users with high quality and low quality of data services simultaneously by controlling hierarchical modulation parameter. Assuming AWGN channel, SNR estimation performance characteristics are investigated considering hierarchical modulation parameter and type of constellation points. From simulation results, it is found that constellation point showing superior SNR estimation performance relative to other points is exist. Also, it is known that according to hierarchical modulation parameter, SNR estimation range with more accurate estimation performance is divided.

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.426-432
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• 2012

In this paper, we investigate the signal to noise ratio (SNR) estimation performance of Cross quadrature amplitude modulation (QAM), which is being used for asymmetric digital subscriber line (ADSL), very high bit rate digital subscriber line (VDSL), and digital video broadcasting - cable (DVB-C), and has been found to be useful in adaptive modulation and blind equalization. At first, the symbol error rate (SER) performance of Cross QAM is analyzed in Rayleigh fading channel. Then we suggest error vector magnitude (EVM) based SNR estimation utilizing the selected constellation points having different types of decision region from one another, and verify that SNR estimation performance of each points have different performance pattern through simulation. From the simulation results, it has been found that when suggested selected constellation points are used for SNR estimation in Cross QAM, estimation performance is enhanced in additive white Gaussian noise (AWGN) channel or Ricean fading channel.

• 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 Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1287-1296
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• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• 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.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.26-30
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• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• 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.