Description: We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers. Platform: |
Size: 1265664 |
Author:吴大亨 |
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Description: 基于多窗谱法功率谱估计的谱减法语音增强程序，多窗谱功率谱估计比传统的周期图法方差效果更好，抑制音乐噪声效果更佳。
该程序对语音增强入门的研究者有参考意义。-Spectrum method based on multi-window power spectral estimate of spectral subtraction speech enhancement procedures, multi-window spectral power spectrum estimation than the traditional periodogram variance better suppress noise better music. The procedure entry on speech enhancement researchers have reference value. Platform: |
Size: 2048 |
Author:武月 |
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Description: 利用最小方差方法估计复正弦加白噪声得平稳信号的功率谱-Minimum variance estimation using complex sinusoidal plus a stationary white noise signal power spectrum Platform: |
Size: 1024 |
Author:yutan |
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Description: Welch法得到的功率谱估计。
设计滤波器对白噪声滤波后的功率谱。-Welch method of power spectrum estimation. Design filters with the noise power spectrum after filtering. Platform: |
Size: 1024 |
Author:ladan |
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Description: 很好的OFDM的基于MATLAB的仿真程序包，且包含了最终结果图.-montecarlo
type montecarlo in the command window and wait for a long time..
_simulation of the complete OFDM system.
_use of a very large file in order to get probabilities.
_loop over different value of the noise.
_compute the SNR for each value of the noise.
_provide the SNR/BER plot.
if you are in a rush : simulation_system !!
go into the right folder and type simulation_system in the command window.
_then type the value of the noise power (range = [-20,10])
_the function provides the channel estimation, the bit allocation,
and a plot illustrating the errors.
_this is a fast function (less pilot, no synchronization, small file). Platform: |
Size: 96256 |
Author:田静 |
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Description: ex6_1 ～ ex6_3二项分布的随机数据的产生
ex6_4 ～ ex6_6通用函数计算概率密度函数值
ex6_7 ～ ex6_20常见分布的密度函数
ex6_21 ～ ex6_33随机变量的数字特征
ex6_34 采用periodogram函数来计算功率谱
ex6_35 利用FFT直接法计算上面噪声信号的功率谱
ex6_36 利用间接法重新计算上例中噪声信号的功率谱
ex6_37 采用tfe函数来进行系统的辨识，并与理想结果进行比较
ex6_38 在置信度为0.95的区间上估计有色噪声x的PSD
ex6_39 在置信度为0.95的区间上估计两个有色噪声x，y之间的CSD
ex6_40 用程序代码来实现Welch方法的功率谱估计
ex6_41 用Welch方法进行PSD估计，并比较当采用不同窗函数时的结果
ex6_42 用Yule-Walker AR法进行PSD估计
ex6_43 用Burg算法计算AR模型的参数
ex6_44 用Burg法PSD估计
ex6_45 比较协方差方法与改进的协方差方法在功率谱估计中的效果
ex6_46 用Multitaper法进行PSD估计
ex6_47 用MUSIC法进行PSD估计
ex6_48 用特征向量法进行PSD估计-ex6_1 ~ ex6_3 binomial distribution of the generated random data
ex6_4 ~ ex6_6 generic function value of the probability density function
ex6_7 ~ ex6_20 common distribution density function
ex6_21 ~ ex6_33 figure characteristics of random variables
periodogram function ex6_34 used to calculate the power spectrum
ex6_35 direct method using FFT signal above the noise of the power spectrum
ex6_36 recalculated using the indirect method on the example of the power spectrum of noise signal
tfe function ex6_37 used for system identification, and results were compared with the ideal
ex6_38 at 0.95 confidence interval for the estimated colored noise x on the PSD
ex6_39 at 0.95 confidence interval of the two colored noise on the estimated x, y between the CSD
ex6_40 code used to achieve the Welch method of power spectrum estimation
Welch method ex6_41 with PSD estimates, and compare different window function when the results when
ex6_42 using Yule-Walker AR method is esti Platform: |
Size: 7168 |
Author:张满超 |
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Description: SPEECH ENHANCEMENT BASED ON WAVELET DENOISING
Abstract: - Noise is an unwanted and inevitable interference in any form of communication. It is
non-informative and plays the role of sucking the intelligence of the original signal. Any kind of
processing of the signal contributes to the noise addition. A signal traveling through the channel
also gathers lots of noise. It degrades the quality of the information signal. The effect of noise
could be reduced only at the cost of the bandwidth of the channel which is again undesired as
bandwidth is a precious resource. Hence to regenerate original signal, it is tried to reduce the
power of the noise signal or in the other way, raise the power level of the informative signal, at
the receiver end this leads to improvement in the signal to noise ratio (SNR). There are several
ways in doing it and here the focus is on adaptive Signal processing new technique (Grazing
Estimation method) to improving the signal to noise ratio.-SPEECH ENHANCEMENT BASED ON WAVELET DENOISING
Abstract:- Noise is an unwanted and inevitable interference in any form of communication. It is
non-informative and plays the role of sucking the intelligence of the original signal. Any kind of
processing of the signal contributes to the noise addition. A signal traveling through the channel
also gathers lots of noise. It degrades the quality of the information signal. The effect of noise
could be reduced only at the cost of the bandwidth of the channel which is again undesired as
bandwidth is a precious resource. Hence to regenerate original signal, it is tried to reduce the
power of the noise signal or in the other way, raise the power level of the informative signal, at
the receiver end this leads to improvement in the signal to noise ratio (SNR). There are several
ways in doing it and here the focus is on adaptive Signal processing new technique (Grazing
Estimation method) to improving the signal to noise ratio. Platform: |
Size: 192512 |
Author:majid |
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Description: Herbordt, W. Nakamura, S. & llermann, W. Multichannel estimation of the power spectral density of noise for mixtures of nonstationary signals IPSJ SIG Technical Reports, 2004 ,131 ,211 - 216-Herbordt, W. Nakamura, S. & llermann, W. Multichannel estimation of the power spectral density of noise for mixtures of nonstationary signals IPSJ SIG Technical Reports, 2004 ,131 ,211- 216 Platform: |
Size: 3072 |
Author:yao wang |
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Description: Phase noise resulting in Common Phase Error
(CPE) and Inter-Carrier Interference (ICI) is a critical challenge
to the implementation of OFDM systems. Modeling phase noise
as a stationary Gaussian random process with the specified
power spectrum density, different from conventional approaches
which mostly relay on pilots to provide CPE estimation, we
explore the statistical characteristics of the sufficient statistics
then propose a pilot-aided decision-directed approach according
to maximum-likelihood criterion. Numerical results demonstrate
that the proposed algorithm enjoys 2dB gain at moderate SNR
and is quite robust against possible model mismatch Platform: |
Size: 148480 |
Author:xiaobo |
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Description: Phase noise resulting in Common Phase Error
(CPE) and Inter-Carrier Interference (ICI) is a critical challenge
to the implementation of OFDM systems. Modeling phase noise
as a stationary Gaussian random process with the specified
power spectrum density, different from conventional approaches
which mostly relay on pilots to provide CPE estimation, we
explore the statistical characteristics of the sufficient statistics
then propose a pilot-aided decision-directed approach according
to maximum-likelihood criterion. Numerical results demonstrate
that the proposed algorithm enjoys 2dB gain at Platform: |
Size: 84992 |
Author:xiaobo |
Hits:
Description: Phase noise resulting in Common Phase Error
(CPE) and Inter-Carrier Interference (ICI) is a critical challenge
to the implementation of OFDM systems. Modeling phase noise
as a stationary Gaussian random process with the specified
power spectrum density, different from conventional approaches
which mostly relay on pilots to provide CPE estimation, we
explore the statistical characteristics of the sufficient statistics
then propose a pilot-aided decision-directed approach according
to maximum-likelihood criterion. Numerical results demonstrate
that the proposed algorithm enjoys 2dB gain at moderate SNR
and is quite robust against possible model mismatch Platform: |
Size: 6144 |
Author:xiaobo |
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Description:
包含M文件，培训和跟踪落实的噪音中描述的算法：
[1] J.S.厄克伦斯和R. Heusdens，“非平稳噪声跟踪基于数据驱动的递归噪声功率的估计”，IEEE期刊。音频，语音卷。 16，第6页。1112年至1123年，2008年8月。
见Description.doc在zip文件。-Contains m-files to train and implement the noise tracking algorithm described in:
[1] J.S. Erkelens and R. Heusdens, "Tracking of nonstationary noise based on data-driven recursive noise power estimation", IEEE Trans. Audio, Speech & Lang. Proc., Vol. 16, No. 6, pp. 1112-1123, August 2008.
See Description.doc in the zip-file.
Platform: |
Size: 128000 |
Author:zaaa |
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Description: 根据” J.S. Erkelens and R. Heusdens, "Tracking of nonstationary noise based on data-driven recursive noise power estimation”所开发的源码-noisetracker based on data-driven recursive noise power estimation Platform: |
Size: 120832 |
Author:jack |
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Description: 对噪声信号中的正弦信号，通过Pisarenko谐波分解方法、Music算法和Esprit算法进行频率估计，信号源是： 其中， ， ， ； 是高斯白噪声，方差为 。使用128个数据样本进行估计。 1、用三种算法进行频率估计，独立运行20次，记录各个方法的估计值，计算均值和方差； 2、增加噪声功率，观察和分析各种方法的性能。-Sinusoidal signal in the noise signal through the Pisarenko harmonic decomposition method, Music algorithms and Esprit frequency estimation algorithm, the signal source is: where,,, a Gaussian white noise, variance. 128 data samples used to estimate. 1, with three kinds of frequency estimation algorithm, run independently 20 times, record the estimated value of each method to calculate the mean and variance 2, the increase in noise power, observation and analysis of the performance of various methods. Platform: |
Size: 2048 |
Author:gab |
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Description: Signal identification represents the task of a receiver
to identify the signal type and its parameters, with applications
to both military and commercial communications. In this paper,
we investigate the identification of spatial multiplexing (SM) and
Alamouti (AL) space-time block code (STBC) with single carrier
frequency division multiple access (SC-FDMA) signals, when
the receiver is equipped with a single antenna. We develop a
discriminating feature based on a fourth-order statistic of the
received signal, as well as a constant false alarm rate decision
criterion which relies on the statistical properties of the feature
estimate. Furthermore, we present the theoretical performance
analysis of the proposed identification algorithm. The algorithm
does not require channel or noise power estimation, modulation
classification, and block synchronization. Simulation results show
the validity of the proposed algorithm, as well as a very good
agreement with the theoretical analysis. Platform: |
Size: 376832 |
Author:c103 |
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Description: Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation的code(Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation matlab code) Platform: |
Size: 593920 |
Author:npulee
|
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Description: In this paper, we investigate blind channel estimation for Multiple Input Multiple Output (MIMO) multi-carrier
CDMA (MC-CDMA) systems in the uplink scenario. An Autocorrelation Contribution Matrix (ACM) method is proposed in
comparison with the subspace approach. Using only second order
statistics, the ACM approach shows similar performance to that
of subspace based approach. The added advantage is that it eliminates the need for rank estimation and noise power calculation
as in subspace technique. In particular we incorporate blind
channel estimation with Layered Space Frequency Equalisation
(LSFE),which employs successive interference cancellation and
therefore provides significant performance improvement over the
conventional linear minimum mean square error (MMSE) based
approach. Platform: |
Size: 109568 |
Author:Casper_z
|
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Search - noise power estimation