Characterisation of Propagation Loss for a 3G Cellular Network in a Crowded Market Area Using CCIR Model

Abstract

Article History KeywordsPropagation loss Cellular network CCIR model RMSE-tuned model Model tuning Empirical model.In this paper, the propagation loss for 1800 MHz cellular network in a crowded market is studied and characterized using the Comit´e International des Radio-Communication, (CCIR) propagation loss model. Empirical measurement of the received signal strength in the market was conducted using CellMapper android app installed on Samsung Galaxy S4 phone. The CCIR model was configured with three different percentages of covered areas (PB). The model was optimized using the root means square error (RMSE) method and also by tuning the PB value. The un-tuned CCIR model gave an RMSE value of 9.23 dB which is above the acceptable upper limit of 6 dB for propagation loss prediction models. On the other hand, the PB-tuned CCIR model gave the best prediction result with an RMSE value of 2.177 dB and prediction accuracy of 98.11 % which is better than the performance of all the RMSE-tuned CCIR models. The results showed that apart from using an RMSE value to tune the CCIR propagation loss model, adjustment of some other key parameters of the model can as well provide a better prediction performance. However, the choice of the parameter to be tuned depends on the specific nature of the case study area.Contribution/Originality: The paper's primary contribution is the development of an alternative approach for optimizing the CCIR model by adjusting the percentage of covered area rather than using the root mean square error (RMSE). The paper demonstrated that the proposed method can give better propagation prediction performance than the RMSE-based optimization approach.International Telecommunication Union (ITU) [18][19][20][21]. The CCIR propagation model combined the effect of the free space propagation loss and the terrain-induced propagation loss. The specific focus of this paper is to derive the tuned CCIR model that will effectively predict the propagation loss in the market with the minimal prediction error and higher prediction accuracy.

Ecosystem Controlling Using Identification of Animal Based on Deep Learning and GLCM (Gray Level Co-occurrence Matrix) Method

“…The wildlife monitoring and guarding system are intended as a regulatory process to balance resource use and conservation and can be sustainable. [1] The type of technology that has been used for animal monitoring is using radio waves and WSN Band the use of GPS (Global Positioning system). This is used for tracking systems that put on cameras to capture moving objects, while the most popular and easy for implementation.…”