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IEEE Final Year Project Topics for CSE

Base Paper Title

Machine-Learning and 3D Point-Cloud BasedSignal Power Path Loss Model forthe Deployment of WirelessCommunication Systems

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IEEE Project Abstract

Modeling signal power path loss (SPPL) for deployment of wireless communicationsystems (WCSs) is one of the most time consuming and expensive processes that require data collectionsduring link budget analysis. Radio frequency (RF) engineers mainly employ either deterministic or stochasticapproaches for the estimation of SPPL. In the case of stochastic approach, empirical propagation models usepredefined estimation parameters for different environments such as reference distance path loss PL(d0)(dB),path loss exponent (n), and log-normal shadowing (Xσ with N(σ, µ = 0)). Since empirical modelsbroadly classify the environment under urban, suburban, and rural area, they do not take into account everymicro-variation on the terrain. Therefore, empirical models deviate significantly from actual measurements.This paper proposes a smart deployment method of WCS to minimize the need for predefined estimationparameters by creating a 3-D deployment environment which takes into account the micro-variations inthe environment. Tree canopies are highly complex structures which create micro-variations and relatedunidentified path loss due to scattering and absorption. Thus, our proposed model will mainly focus on theeffect of tree canopies and can be applied to any environment. The proposed model uses a 2-D image colorclassification to extract features from a 3-D point cloud and a machine learning (ML) algorithm to predictSPPL. Empirical path loss models have received signal level (RSL) errors in the range of 6.29%-16.9% fromthe actual RSL measurements while the proposed model has an RSL error of 4.26%.

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