Cognitive radio is the enabling technology for license-exempt access to the TV White Spaces (TVWS). There is ever increasing demand of users in the broadcasting and communication services. Large portions of unused spectrum in the UHF/VHF bands exist in India which can be used on geographical basis. This paper describes a study on path loss variation in UHF/VHF bands in India. The aim of this study is to develop and optimize a path loss model based on Linear minimum mean square error estimation (LMMSE) for India. We propose the LMMSE based Optimized Perez-Vega Zamanillo propagation path loss model. The measured path loss values, collected across India, are compared with proposed Optimized Perez-Vega Zamanillo path loss model and other existing path loss models. It is found that Optimized Perez-Vega Zamanillo propagation path loss model has the least root mean square Error (RMSE) of 13.98 dB. Other existing path loss models have root mean square Error(RMSE) value greater than 24 dB. Therefore, Optimized Perez-Vega Zamanillo propagation path loss model is best suited for predicting coverage area, interference analysis in India for TVWS.
We have developed a path-loss model that includes environmental variables. We take a sizeable 2-dimensional satellite image of 4 cities, namely Hyderabad, Mumbai, Chennai, New Delhi, and then divide the large 2d image into many smaller images. Then we perform image segmentation using the Maximum likelihood algorithm on each smaller image. Segmentation separates the image into separate areas comprising of pixels with identical qualities. After that, we develop three different 11 input path loss models based on Fuzzy logic, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), respectively. Input Parameters to all these three path loss models were %building, %road, %plain, %water, %trees, transmitter terrain height, receiver terrain height, the distance between receiver and transmitter, average clutter height, transmitter frequency, and transmitter height. The output of all the above three models is a path loss. We acquired receiver power levels data in a driving test through different routes in all four cities. We compared measured path-loss values for each route with the predicted values obtained with ANN(with image segmentation), ANFIS(with image segmentation), FCM(with image segmentation), ANFIS(without image segmentation), and empirical path loss models. We measured each path-loss model's accuracy with RMSE (root mean square error) obtained between the predicted & measured path loss values. This paper found that ANFIS(with image segmentation) path-loss model has an RMSE of 2.16 dB, the lowest RMSE among all the considered path-loss models.
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