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Fig.4: The building geometry and parameters in Bertoni- Walfisch Model. Bertoni-Walfisch proposed a semi empirical model that is applicable to propagation though buildings in urban environments. Which, it has been adopted here due to its simplicity and validity which has been verified with measurements [10]. The model assumes building heights to be uniformly distributed and the separation between buildings are equal. Propagation is then equated to the process of multiple-diffraction past these rows of buildings. The building geometry and parameters in Bertoni-Walfisch model are illustrated in Fig. 4.

Figure 4 The building geometry and parameters in Bertoni- Walfisch Model. Bertoni-Walfisch proposed a semi empirical model that is applicable to propagation though buildings in urban environments. Which, it has been adopted here due to its simplicity and validity which has been verified with measurements [10]. The model assumes building heights to be uniformly distributed and the separation between buildings are equal. Propagation is then equated to the process of multiple-diffraction past these rows of buildings. The building geometry and parameters in Bertoni-Walfisch model are illustrated in Fig. 4.

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The coverage prediction map of Standard macrocell model based in Hata model using ASSET software with received power (dBm) sampling is shown in Fig.2 Fig.2: Prediction map of BS1 for ASSET software and drive test samples.
The coverage prediction map of Standard macrocell model based in Hata model using ASSET software with received power (dBm) sampling is shown in Fig.2 Fig.2: Prediction map of BS1 for ASSET software and drive test samples.
Table 1: Macrocell information. Due to a last fail test, we need to repeat this test with the same cell. But at this time, we turned off the neighboring 6 cells which have the same frequency at midnight. Where we cannot turning off the cells during day, because cells are busy so the most of capacity of cells are full enough. So, the number of samples became larger and the range of distance is wider. The received power ranges from -35 dBm to -120 dBm with distance ranges between 4 m and 521 m with 50748 samples. The specifications of the tested base station are illustrated in Table 1.
Table 1: Macrocell information. Due to a last fail test, we need to repeat this test with the same cell. But at this time, we turned off the neighboring 6 cells which have the same frequency at midnight. Where we cannot turning off the cells during day, because cells are busy so the most of capacity of cells are full enough. So, the number of samples became larger and the range of distance is wider. The received power ranges from -35 dBm to -120 dBm with distance ranges between 4 m and 521 m with 50748 samples. The specifications of the tested base station are illustrated in Table 1.
Fig.1: Received power distribution for BS1. The measured values of the received signal strength are illustrated in Fig. 1.
Fig.1: Received power distribution for BS1. The measured values of the received signal strength are illustrated in Fig. 1.
Table 2: Path Loss Exponents for Different Environments
Table 2: Path Loss Exponents for Different Environments
The Simulation results for this propagation model and the measured values are illustrated in Fig. 3. Moreover, applying this model for other cells to find path loss exponent n is illustrated in Table 3, where the main statistical indications (Mean Error up, Standard Deviation o, Root Mean Square Error (RMSE)) for Log-distance propagation model values compared to the measured ones are illustrated in Table 4. Fig.3: Log-distance model at n=3.86 and measured data.
The Simulation results for this propagation model and the measured values are illustrated in Fig. 3. Moreover, applying this model for other cells to find path loss exponent n is illustrated in Table 3, where the main statistical indications (Mean Error up, Standard Deviation o, Root Mean Square Error (RMSE)) for Log-distance propagation model values compared to the measured ones are illustrated in Table 4. Fig.3: Log-distance model at n=3.86 and measured data.
Table 3: Path loss exponent for different base stations.
Table 3: Path loss exponent for different base stations.
Table 4: Statistical values for Log-distance propagation model applied for certain base stations.
Table 4: Statistical values for Log-distance propagation model applied for certain base stations.
Fig.5: Block diagram of optimization process. 4.1 STANDARD MACROCELL MODEL TUNING The optimization, or tuning, process is based on the measured data using least square method. Statistical analysis such as mean error, standard deviation and root mean error were used. Comparison between optimized model and other models were compared according to these statistical values. An illustrative block diagram is shown in Fig.5 [11].
Fig.5: Block diagram of optimization process. 4.1 STANDARD MACROCELL MODEL TUNING The optimization, or tuning, process is based on the measured data using least square method. Statistical analysis such as mean error, standard deviation and root mean error were used. Comparison between optimized model and other models were compared according to these statistical values. An illustrative block diagram is shown in Fig.5 [11].
International Journal of Computer Applications (0975 — 8887) Volume 60- No.5, December 2012 before and after tuning for BS1. 4.2 BERTONI-WALFISCH MODEL TUNING
International Journal of Computer Applications (0975 — 8887) Volume 60- No.5, December 2012 before and after tuning for BS1. 4.2 BERTONI-WALFISCH MODEL TUNING
Based on the Bertoni-Walfisch model, the Simulation results are illustrated for the original and tuned model in terms of path loss as shown in Fig. 7. From Eq. (7), one can see that, the path loss increases with distance at a rate of 23 dB per decade in Bertoni-Walfisch model after tuning. Fig. 7: Path loss comparison of Bertoni-Walfisch model before and after tuning.
Based on the Bertoni-Walfisch model, the Simulation results are illustrated for the original and tuned model in terms of path loss as shown in Fig. 7. From Eq. (7), one can see that, the path loss increases with distance at a rate of 23 dB per decade in Bertoni-Walfisch model after tuning. Fig. 7: Path loss comparison of Bertoni-Walfisch model before and after tuning.
Table 7 Mean error, standard deviation and RMSE comparison for Bertoni-Walfisch and Standard Macrocell models 5. Simulation and Results
Table 7 Mean error, standard deviation and RMSE comparison for Bertoni-Walfisch and Standard Macrocell models 5. Simulation and Results
Table 8: Mean Error comparison between tuned Bertoni- Walfisch models with known models
Table 8: Mean Error comparison between tuned Bertoni- Walfisch models with known models
Table 9: Standard Deviation comparison for tuned Bertoni-Walfisch model with known models
Table 9: Standard Deviation comparison for tuned Bertoni-Walfisch model with known models
The tuned Bertoni-walfisch model has been applied for 8 base stations in Nablus. Where, multiple cell implementations have been conducted. The performance has been compared to several other models like Hata, Walfish-Ikegami, Hata and standard Macrocell. Comparison has been performed in terms of error and standard deviation as shown in Table 8 and Table 9 respectively. The comparison shows that the proposed tuned Bertoni-Walfisch tuned model is suitable for almost all the base stations since the error was reduced significantly. The proposed tuned model has been compared to other models as shown in Fig.8. The mean error of BS1 has been reduced to zero and the standard deviation was decreased by 13.12% and the average standard deviation is 8.47. Fig.8: Path loss comparison between measured data for BS 1 and propagation models.
The tuned Bertoni-walfisch model has been applied for 8 base stations in Nablus. Where, multiple cell implementations have been conducted. The performance has been compared to several other models like Hata, Walfish-Ikegami, Hata and standard Macrocell. Comparison has been performed in terms of error and standard deviation as shown in Table 8 and Table 9 respectively. The comparison shows that the proposed tuned Bertoni-Walfisch tuned model is suitable for almost all the base stations since the error was reduced significantly. The proposed tuned model has been compared to other models as shown in Fig.8. The mean error of BS1 has been reduced to zero and the standard deviation was decreased by 13.12% and the average standard deviation is 8.47. Fig.8: Path loss comparison between measured data for BS 1 and propagation models.
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