An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments. - Mohamed K. Elmezughi; Thomas J. Afullo (IEEE Access, 2021)

A Machine-Learning-Based Labelling Diversity Model for Predictive Analysis: Using 16QAM as a Case Study. - Shaheen Solwa; Mohamed K. Elmezughi; Omran Salih; Ali M. Almaktoof; M. T. E. Kahn (IEEE Access, 2022)

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels. - Mohamed K. Elmezughi; Omran Salih; Thomas J. Afullo; Kevin Jan Duffy (Sensors, 2022)

Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz. - Tolulope T. Oladimeji; Pradeep Kumar 004; Mohamed K. Elmezughi (Sensors, 2022)

Performance Analysis of High Order Close-In Path Loss Model at 28 and 38 GHz. - Tolulope T. Oladimeji; Pradeep Kumar 004; Mohamed K. Elmezughi (ICTAS, 2023)

An improved path loss model for 5G wireless networks in an enclosed hallway. - Tolulope T. Oladimeji; Pradeep Kumar 004; Mohamed K. Elmezughi (Wirel. Networks, 2024)