A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated

Hata-Okumura Model Computer Analysis for Path Loss Determination at 900MHz for Maiduguri, Nigeria

Empirical propagation models are used extensively for coverage prediction during the design and planning of wireless networks. Some of the most widely used empirical models include the COST 231 Hata Model (COST 231 1999, Saunders 2000, COST 231 revision 2), COST 231-Walfisch-Ikegami Model (COST 231 1999), etc. These models, however, are not universally applicable due differences in terrain clutter. Thus, when planning a wireless communication network it is necessary to determine radio propagation characteristics optimal to the terrain in question. In this paper, the applicability of the COST 231 Hata Model to the metropolis of Maiduguri, Nigeria, is tested by computing variations between the COST 231 Hata predictions and predictions based on the Least Squares function, being the best fit curve through measured data points. This was achieved with the help of a software system comprising of Visual Basic as Front End and Microsoft Excel as Back End. The Root Mean Square Error (RMSE) was found to be 5.33dB, which is acceptable, the acceptable maximum being 6dB. Further statistical proof testifies to the acceptability of the COST 231 Hata Model for path loss prediction across the metropolis of Maiduguri, Nigeria. Keywords: COST 231 Hata, Hata-Okumura Model, COST 231-Walfisch-Ikegami Model, Root Mean Square Error, , Mean Prediction Error

A Machine-Learning Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments

International audienceWe evaluate the accuracy of a machine-learning-based path loss model trained on 42,157,324 RSSI samples collected over one year from an environmental wireless sensor network using 2.4 GHz radios. The 2218 links in the network span a 2000 km 2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms were evaluated in order to find the one with lowest error: Random Forest, Adaboost, Neural Networks, and K-Neareast-Neighbors. Of the candidate models, Random Forest showed the lowest error. The independent variables used in the model include path distance, canopy coverage, terrain variability, and path angle. We compare the accuracy of this model to several well-known canonical (Free Space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). Unlike canonical models, machine-learning algorithms are not problem-specific: they rely on an extensive dataset and a flexible model architecture to make predictions. We show how this model achieves a 37% reduction in the average prediction error compared to the canonical/empirical model with the best performance. The article presents a in-depth discussion on the strengths and limitations of the proposed approach as well as opportunities for further research