Increasing numbers of the mobile communication users in urban city; especially indoor users cause the radio coverage prediction of outdoor to indoor becoming important. Obstacles are the main problems for outdoor to indoor propagation that weaken signal level and worsen information detection. This paper proposes a combination of COST231 Walfisch-Ikegami and COST231 Multiwall to predict the received signal. By comparing the predicted and the measured signal level in the Antara building, Medan city for both 1800 MHz and 2100 MHz channels, the proposed model outperforms the compared method in predicting signal level. The proposed model is able to suppress the prediction deviation 11.035 dB lower than the compared method for Sector A and 5.98 dB lower at Sector B.
Wireless internet service in educational buildings plays a crucial role in telecommunications for the knowledge sharing process. Therefore, various factors that may limit internet services coverage in the building should be eliminated or reduced. One such factor is path losses. Path losses are caused by multiple obstacles between the transmitting and receiving antennas. The problem of path losses in the education building can be solved by providing signal booster devices or Wireless Fidelity (Wi-Fi). But not all college buildings have such tools. Besides, WiFi devices also have limitations on bandwidth and the number of users. Thus, mobile communication devices or smartphones located inside the education buildings still need internet service coverage from the transmitter antenna outside the building. An accurate propagation model is required so that the transmitter antenna outside the building can provide internet service coverage to the inside of the building. This paper had been analyzed the selection of propagation models using three validation formulas, namely Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE). This paper used several propagation models, namely the 3GPP Model, Winner+ Model, and COST231 Model. Based on the analysis of calculation and measurement data, it is known that the COST231 model is the most accurate because it has the lowest ME, RMSE, and SDE values.
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