SHOEWU OMOTAYO OLUWAGBEMIGA

Electronics and Computer Engineering

At the Electronics And Computer Engineering department office

Appointment on Visitation important

Topic: Deep Learning-Based Path Loss Models For Classified Outdoor Propagation

Description: Path loss modeling is a critical component of wireless communication system design, as it enables accurate prediction of signal attenuation over distance. Traditional path loss models, such as the Okumura-Hata model, are based on empirical formulas and may not accurately capture the complexities of outdoor propagation environments. Recent advances in deep learning have shown promise in improving path loss modeling accuracy.Research ObjectivesThe main objective of this research is to develop deep learning-based path loss models for classified outdoor propagation environments. The specific objectives are:1. Data Collection: Collect a comprehensive dataset of path loss measurements for various outdoor propagation environments, including urban, suburban, and rural areas.2. Deep Learning Model Development: Develop and train deep learning models, such as convolutional neural networks CNNs and recurrent neural networks RNNs , to predict path loss based on input features such as frequency, distance, and environment type.3. Model Evaluation: Evaluate the performance of the developed deep learning models using metrics such as mean absolute error MAE and root mean square error RMSE .4. Comparison with Traditional Models: Compare the performance of the developed deep learning models with traditional path loss models, such as the Okumura-Hata model.MethodologyThe proposed methodology involves the following steps:1. Data Collection: Collect path loss measurements using a combination of measurement campaigns and publicly available datasets.2. Data Preprocessing: Preprocess the collected data by converting it into a suitable format for deep learning model development.3. Deep Learning Model Development: Develop and train deep learning models using the preprocessed data.4. Model Evaluation: Evaluate the performance of the developed deep learning models using metrics such as MAE and RMSE.5. Comparison with Traditional Models: Compare the performance of the developed deep learning models with traditional path loss models.Expected OutcomesThe expected outcomes of this research are:1. Improved Path Loss Modeling Accuracy: Develop deep learning-based path loss models that achieve higher accuracy than traditional models.2. Classification of Outdoor Propagation Environments: Develop a classification system for outdoor propagation environments based on deep learning-based path loss models.3. Contribution to the State-of-the-Art: Contribute to the state-of-the-art in path loss modeling by developing novel deep learning-based models.

#	Certificate	School	Year
1.	Ph.D (Electrical Engineering Electronics and Communications Engineering )	Department of E;lectrical/Electronics Engineering, Michael Okpara University of Agriculture, Umudike	2021

Ab initio Quantum Physics Based Dielectric function for irregular Geometry Using Conservative Finite Difference Method

Effect of Electromagnetic waves on Off-shore and On-shore in Vegetational Environment. Plasmonic devices with accelerated computing in Dielectric Material coupled with Electromagnetic Signals. Ab initio Quantum Physics-Based Dielectric function for Irregular Geometry Using Conservative Finite Difference Method Due to non-comprehensive numerical technique for modelling and simulation of plasmonic device applications and particularly small-scale devices as a result of scaling of semiconductor devices from macro to micro up to nano region, there is an urgent need for exploration and investigation of different models applicable in this field of study. In this work, efforts will be geared towards addressing this lacuna by applying a novel numerical technique, the Conservative Finite Difference Method to the modelling and simulation of frequency-dependent dielectric function in plasmonic and small-scale device applications for the next generation. This research comprises analytical derivations, ab initio modelling as well as computer simulations. The approaches adopted in this study include analytical and theoretical formulations, Standard Finite Difference Method, and Conservative Finite Difference Method CFDM for the simulation of devices. The merit of using a conservative numerical scheme such as CFDM is that it preserves the original properties of the differential operator involved in discretization, Computer simulations using MATLAB and other software tools will be presented in evaluating the developed models.

SHOEWU OLUWAGBEMIGA is a Senior Lecturer at the Department of Electronics and Computer Engineering

SHOEWU has a Ph.D in Electrical Engineering Electronics and Communications Engineering from Department of E;lectrical/Electronics Engineering, Michael Okpara University of Agriculture, Umudike