Large scale survey for radio propagation in developing machine learning model for path losses in communication systems

• DOI: 10.1016/j.sciaf.2023.e01550
• Publication Date: 2023-01-12T07:37:08Z
• Authors: Haruna Chiroma; Ponman Nickolas; Nasir Faruk; Emmanuel Alozie; Imam-Fulani Yusuf...; Kayode S. Adewole; Abubakar Abdulkarim; Abdulkarim A. Olo...; Olugbenga A. Sowande; Salisu Garba; Aliyu D. Usman; Lawan S. Taura; Yinusa A. Adediran
• Keywords: Robustness

Several orthodox approaches, such as empirical methods and deterministic methods, had earlier been used for the prediction of path loss in wireless communication systems. These approaches are either inefficient or complex. Robustness performance motivated adoption machine learning modeling systems place traditional schemes. Surveys on exist literature; however, emerging deep architectures in-depth analysis, taxonomies related to loss, analysis feature engineering missing already published surveys. To fill this existing gap, a survey is conducted resolve outlined issues, hence making unique. Synthesis solve problems hereby presented. New taxonomy – learning, nature-inspired meta-heuristic algorithms, shallow algorithms approach have created. Analysis exploited. Lastly, challenges militating against full potential based highlighted discussed. Alternative resolving also presented help designing more practical applications future.