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: 1265450 |
Author:吴大亨 |
Hits:
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 |
Hits:
Description: 这篇文档重在介绍多载波的MIMO技术中如何进行肓信道估计,具有很高的价值.-Introduction This document focus on the MIMO multi-carrier technology in how to proceed with blind channel estimation of high value. Platform: |
Size: 366592 |
Author:wanghuan |
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Description: This file contains a matlab file for blind carrier estimation which compute the phase and frequency offset of carrier due to Doppler shift. Platform: |
Size: 1024 |
Author:Abbas |
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Description: 经典的CPM信号的载波、符号率、调制指数联合盲估计的IEEE文章-Classic CPM signal carrier, symbol rate, modulation index of the IEEE Joint blind estimation of the article Platform: |
Size: 369664 |
Author:王威 |
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Description: 一种关于MIMO-OFDM系统盲频率同步的资料文档，用MATLAB进行仿真分析-A novel blind carrier frequency offset (CFO)
estimation algorithm was proposed which is based on estimating
signal parameter via rotational invariance techniques (ESPRIT)
for multi-input multi-output orthogonal frequency division
multiplexing (MIMO-OFDM) systems with virtual carriers. Platform: |
Size: 402432 |
Author:李真 |
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Description: 可以得到很精确的幅值、频率、相位估计，采用累计贡献率的方法，有小波分析的盲信号处理，抑制载波型差分相位调制，预报误差法参数辨识-松弛的思想，用MATLAB实现的压缩传感。- You can get a very accurate amplitude, frequency, phase estimation, The method of cumulative contribution rate There Wavelet Analysis Blind Signal Processing, Suppressed carrier type differential phase modulation, Prediction Error Method for Parameter Identification- the idea of relaxation, Using MATLAB compressed sensing. Platform: |
Size: 11264 |
Author:nhtvebg |
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Description: 盲多载波频偏估计算法，使用过采样算法。估计载波频率偏移-Blind Estimation of multiple carrier frequency offset. The goal of this Matlab script is to identify a Multiple antenna system with multiple Carrier Frequency Offsets based on over-sampling. This code will plot
the blind estimation results using figures. Platform: |
Size: 10240 |
Author:hou weikun |
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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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