Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model
Nnadi Nathaniel Chimaobi,
Ifeanyi Chima Nnadi,
Chibuzo Promise Nkwocha
Issue:
Volume 5, Issue 3, May 2017
Pages:
19-23
Received:
8 January 2017
Accepted:
24 January 2017
Published:
14 June 2017
Abstract: Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.
Abstract: Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residu...
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Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments
Issue:
Volume 5, Issue 3, May 2017
Pages:
24-28
Received:
7 August 2017
Published:
7 August 2017
Abstract: Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.
Abstract: Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual ...
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