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ML estimation of time and frequency offset in OFDM systems
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In this paper, we present a novel data-based method for simultaneous Maximum Likelihood
(ML) symbol and carrier-frequency o畇et estimation in Orthogonal frequencydivision
multiplexing (OFDM) systems. Statistical properties introduced by the cyclic
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OFDM时频ML信道估计算法-OFDM time-frequency channel estimation algorithm for ML
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利用ML算法进行载波频偏的估计，并完成整个ofdm系统的简单仿真-Carrier frequency offset estimation using ML algorithm, a simple simulation and complete the whole OFDM system
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STBC OFDM (included ML MAP estimation),Alamouti_FD, CIR_estimation, Tx_data_signal_generator_Alamouti_FD
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对OFDM的载波频偏进行估计和补偿，采用ML最大似然估计算法，有不同频偏值和不同信噪比的仿真结果图。-For OFDM carrier frequency offset estimation and compensation, using ML maximum likelihood estimation, have different values and different SNR offset simulation results shown in Fig.
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包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，ML法能够很好的估计信号的信噪比,ofdm系统仿真 含16qam调制 fft 加窗 加cp等模块。- Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, ML estimation method can be a good signal to noise ratio, ofdm system simulation including 16qam
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STBC OFDM (included ML MAP estimation),Alamouti_FD, CIR_estimation, Tx_data_signal_generator_Alamouti_FD
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To achieve the recognition of 10 digital sound, ML estimation method can be a good signal to noise ratio, Algorithm for researchers to do reference.
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ML estimation method can be a good signal to noise ratio, ECG data and includes source code written in MATLAB, There Wavelet Analysis Blind Signal Processing.
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