A BLIND SNR ESTIMATOR FOR DIGITAL BANDPASS SIGNALS

A subspace-based blind Signal-to-Noise Ratio (SNR) estimation algorithm for digital bandpass signals in Additive White Gaussian Noise (AWGN) channel is discussed. The lower bounds of the mean and variance of the estimation are derived, and simulations are performed for the commonly used digital bandpass signals, such as MPSK (M=2, 4, 8), MFSK (M=2, 4) and MQAM (M=16, 64, 128, 256) signals. Theoretical analyses and simulation results indicate that the proposed algorithm is ef- fective even when the SNR is below 0dB. Furthermore, the algorithm can provide a blind estimator in that it needs neither the parameters of the received signals, such as the carrier frequency, symbol rate and modulation scheme, nor the synchronization of the system.

A NOVEL METHOD FOR ESTIMATING THE SNR OF UNKNOWN EMITTER SIGNAL

It is important to estimate the Signal-to-Noise Ratio(SNR) of unknown emitter signal accurately.In order to resolve the disadvantages of present algorithm,a novel method is proposed in this letter.We extract and normalize the information of zero frequency of received signal by the Wigner-Vile Distribution(WVD) transformation and then get the approximate power of original signal by mathematic transformation,at last,we get the estimate value of SNR by the known account formula of SNR.Simulation results show that it is correct and feasible.

Compressed SNR-and-Channel Estimation for Beam Tracking in 60-GHz WLAN

Signal-to-noise ratio （SNR） and channel estimations are critical for 60-GHz communications to track the optimal trans- mission and reception beam pairs. However, the excessive pilot overhead for the estima- tions severely reduces system throughput in fast-rotation scenarios. In order to address this problem, we firstly demonstrate the potential sparseness property of 60-GHz channel in beam tracking; subsequently, via exploiting this property, we propose a novel compressed SNR-and-channel estimation. The estimation is conducted in a three-stage fashion, includ- ing the unstructured estimation, nonzero-tap detection, and structured estimation with non- zero-tap location. Numerical simulations show that, in the case of substantial reduction of the pilot overhead, the proposed estimator still reveals a significant improvement in terms of estimation performance over the scheme in IEEE 802.1 lad. Furthermore, it is also demon- strated that the proposed SNR and channel estimators can approach the lower bounds in sparse channels so long as SNR exceeds 8 dB.

Parametric SNR Estimation Based on Auto-Regressive Model in AWGN Channels

Signal-to-noise ratio（SNR）estimation for signal which can be modeled by Auto-regressive（AR）process is studied in this paper.First,the conventional frequency domain method is introduced to estimate the SNR for the received signal in additive white Gauss noise（AWGN）channel.Then a parametric SNR estimation algorithm is proposed by taking advantage of the AR model information of the received signal.The simulation results show that the proposed parametric method has better performance than the conventional frequency doma in method in case of AWGN channel.

Switching Equalization Algorithm Based on a New SNR Estimation Method

It is well-known that turbo equalization with the max-log-map （MLM） rather than the log-map （LM） algorithm is insensitive to signal to noise ratio （SNR） mismatch. As our first contribution, an improved MLM algorithm called scaled max-log-map （SMLM） algorithm is presented. Simulation results show that the SMLM scheme can dramatically outperform the MLM without sacrificing the robustness against SNR mismatch. Unfortunately, its performance is still inferior to that of the LM algorithm with exact SNR knowledge over the class of high-loss channels. As our second contribution, a switching turbo equalization scheme, which switches between the SMLM and LM schemes, is proposed to practically close the performance gap. It is based on a novel way to estimate the SNR from the reliability values of the extrinsic information of the SMLM algorithm.

A novel adaptive classification scheme for digital modulations in satellite communication

To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios （SNRs）, a novel adaptive modulation classification scheme is presented in this paper. Differ-ent from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines （SVM） in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types （i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK） at SNRS ranging from 0dB to 25dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications.

Single-channel speech enhancement method based on masking properties and minimum statistics

A single-channel speech enhancement method of noisy speech signals at very low signal-to-noise ratios is presented, which is based on masking properties of the human auditory system and power spectral density estimation of non stationary noise. It allows for an automatic adaptation in time and frequency of the parametric enhancement system, and finds the best tradeoff among the amount of noise reduction, the speech distortion, and the level of musical residual noise based on a criterion correlated with perception and SNR. This leads to a significant reduction of the unnatural structure of the residual noise. The results with several noise types show that the enhanced speech is more pleasant to a human listener.

Channel Estimation and Compensation for Underwater Acoustic Pipeline Communication

An estimation and compensation algorithm for underwater acoustic pipeline channel is investigated.A joint time-frequency adaptive signal-to-noise ratio（SNR）estimation based on the maximum likelihood method is introduced firstly,and the Cramer-Rao lower bound（CRLB）is proposed so as to evaluate the performance of the SNR estimation algorithm.For frequency-selective fading channel part,estimation and compensation are made to improve the robustness of the system on the basis of the LMS algorithm.Furthermore,real-time update iteration algorithm in the frequency domain is investigated to realize synchronous receiving and estimation.For verification,simulations and actual data tests were made,and the results show that the algorithm possesses great robustness,efficiency and accuracy inrealization of SNR estimation,signal detection and frequency impulse compensation for the channel.

SNR estimation algorithm based on the preamble for wireless OFDM systems

Based on the analysis of the performance of Boumard＇s SNR method for wireless orthogonal frequency division multiplexing （OFDM） systems, a new estimation algorithm of the noise variance is proposed by only using the data samples of the two training symbols in the preamble, and the second order moment of these data samples is employed to estimate the signal power. The average SNR and the SNRs on the subchannels can all be estimated by the proposed algorithm, and its performance is independent of the channel＇s frequency selectivity. Simulation results show that the performance of the proposed method is highly improved and much better than that of Boumard＇s method.

Speech enhancement based on modified a priori SNR estimation

To solve the frame delay problem and match the previous frame,Plapous et al.[IEEE Transactions on Audio,Speech,and Language Processing,2006,14(6):2098–2108]introduced a novel approach called two-step noise reduction(TSNR)technique to improve the performance of the speech enhancement system.However,TSNR approach results in spectral peaks of short duration and the broken spectral outlier,which degrade the spectral characteristics of the speech.To solve this problem,a cepstral smoothing step is added in order to remove these spectral peaks brought by TSNR approach.Theory analysis shows that the proposed approach can effectively smooth the spectral peaks and keep the spectral outlier so as to protect the speech characteristics.Experiment results also show that the proposed approach can bring significant improvement compared to decision-directed(DD)and TSNR approaches,especially in non-stationary noisy environments.

Sensitivity analysis of the channel estimation deviation to the MAP decoding algorithm

As a necessary input parameter for maximum a-posteriori(MAP)decoding algorithm,SNR is normally obtained from the channel estimation unit.Corresponding research indicated that SNR estimation deviation degraded the performance of Turbo decoding significantly.In this paper,MAP decoding algorithm with SNR estimation de-viation was investigated in detail,and the degradation mechanism of Turbo decoding was explained analytically.The theoretical analysis and computer simulation disclosed the specific reasons for the performance degradation when SNR estimation was less than the actual value,and for the higher sensitivity of SNR estimation to long-frame Turbo codes.