scholarly journals Estimasi Carrier Ferquency Offset menggunakan Timing Metric pada Sinyal OFDM

2021 ◽  
Vol 5 (4) ◽  
pp. 466
Author(s):  
Yolen Perdana Sari ◽  
Shelvi Eka Tassia
Keyword(s):  
Mean Square Error ◽  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Estimation Method ◽  
Frequency Offset ◽  
Statistical Characteristics ◽  
Estimation Accuracy ◽  
Mean Square ◽  
Ofdm Signal ◽  
The Mean

OFDM is one of technology that can be utilized in a variety of telecommunication systems that being widely developed today, for application in LAN, WLAN, 3G, 4G,  or  5G. One of the problem faced by the OFDM technology that its sensitivity to Carrier Frequency Offset (CFO) and the lack of synchronization in the OFDM signal. This research aims to design the synchronization that estimates Carrier Frequency Offset (CFO) to obtain synchronization of OFDM signal, where the error of the estimated Carrier Frequency Offset can be obtained, minimized and better than previous studies. The CFO estimation method  in this research is using the training symbol on the OFDM symbol and utilize the statistical characteristics of the timing metric. This researchs result shows the Mean Square Error (MSE) of estimated Carrier Frequency Offset to Carrier Frequency Offset input, with range MSE 9.43 x 10-3 at 0 dB SNR input and MSE 1.687 x 10-5 at 30 dB SNR input. If Signal to Noise Ratio is greater, then the value of the mean square error (MSE) will be smaller. The position of the timing metric for timing estimation also affects to CFO estimation. CFO estimation accuracy will be maximized when using maximum timing metric.

A Simplified Carrier Frequency Offset Estimation Algorithm for Burst Digital Transmission

2012 ◽  
Vol 239-240 ◽  
pp. 1255-1258
Author(s):  
Xin Man ◽  
Hai Tao Zhai ◽  
Er Yang Zhang
Keyword(s):  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Estimation Method ◽  
Digital Transmission ◽  
Estimation Algorithm ◽  
Frequency Offset ◽  
Estimation Accuracy ◽  
Frequency Offset Estimation ◽  
Carrier Frequency Offset Estimation ◽  
Estimation Range

In this letter, we present a carrier frequency offset estimation method for burst digital transmission, by introducing a step factor into an earlier estimation algorithm to reduce the computational complexity, with little loss of the estimation accuracy. Performance simulations show that this method can separate the estimation range and accuracy, avoiding the weakness of reaching large estimation range at the expense of estimation accuracy.

scholarly journals An Estimation of a Hybrid Log-Poisson Regression Using a Quadratic Optimization Program for Optimal Loss Reserving in Insurance

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Apollinaire Woundjiagué ◽  
Martin Le Doux Mbele Bidima ◽  
Ronald Waweru Mwangi
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Poisson Regression ◽  
Estimation Method ◽  
Error Prediction ◽  
Mean Square ◽  
Poisson Regression Model ◽  
Loss Reserving ◽  
The Mean

In this article, we are interested in developing an alternative estimation method of the parameters of the hybrid log-Poisson regression model. In our previous paper, we have proposed a hybrid log-Poisson regression model where we have derived the analytical expression of the fuzzy parameters. We found that the hybrid model provide better results than the classical log-Poisson regression model according to the mean square error prediction and the goodness of fit index. However, nowhere we have taken into account the optimal value of h(α-cut) which is of greatest importance in fuzzy regressions literature. In this paper, we provide an alternative estimation method of our hybrid model using a quadratic optimization program and the optimized h-value (α-cut). The expected value of fuzzy number is used as a defuzzification procedure to move from fuzzy values to crisp values. We perform the hybrid model with the alternative estimation we are suggesting on two different numerical data to predict incremental payments in loss reserving. From the mean square error prediction, we prove that the alternative estimation of the new hybrid model with an optimized h-value predicts incremental payments better than the classical log-Poisson regression model as well as the same hybrid model with analytical estimation of parameters. Hence we have optimized the outstanding loss reserves.

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