Abstract
Careful network planning has become increasingly critical with the rising deployment, coverage, and congestion of wireless local area networks (WLANs).This paper investigates and determine the Path-loss exponent value for the ubiquitous wireless local area network at the Federal University Oye-Ekiti for the line of sight and non-line of sight (N-LOS). Aside this, the paper also models the wireless network using artificial neural network (ANN) technology by training some neurons based on data collected from a drive-test.
 The proposed ANN model performed with accuracy and is offered as a simple, yet strong predictive model for network planning – having both speed and accuracy. Results show, that for the area under study, Oye Campus has a higher standard deviation of 5.76dBm as against ikole Campus with 1.44dBm, this is because of dense vegetation at Oye Campus.
 In view of this, the paper provides a predictive site survey for rapid wireless Access point deployment.
Highlights
Path Loss is difference in dB between transmitted signal and received signal strength at a location
IEEE802.11n is a fixed wireless network, the Artificial Neural Network (ANN) model presented in this paper has the following parameters: distance between WiFi AP and the receiver, carrier frequency, height of the WiFi AP and height of the receiver
The research goal was to analyze the behavior of propagation channel, determine the Path loss exponent and to model the channel using ANN for wireless data communication systems operation at ISM 2.4GHz
Summary
Path Loss is difference in dB between transmitted signal and received signal strength at a location. Network Engineers mostly used path loss model as a mathematical tool to determine received signal strength at a given point. The problem of predicting propagation loss between two points may be seen as a function of several inputs and a single output. The introduction of Machine learning techniques has helped in solving complex problems in our everyday life, this can be exploited for path loss predictions in given propagation environments. The capability of ANNs to model complex nonlinear functional relationships provides an opportunity to combine the gains of empirical and deterministic models and to provide better computational efficiency. The feed-forward neural networks [4], [5] are very well suited for prediction purposes because they do not allow any feedback from the output (field strength or path loss) to the input. ANN can be used to model the mathematical function of Pathloss of a given environment
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