Article


Article Code : 13981112215138

Article Title : A Fast Machine Learning for 5G Beam Selection for Unmanned Aerial Vehicle Applications

Journal Number : 28 Autumn 2019

Visited : 78

Files : 1.09 MB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Wasswa Shafik wasswashafik@stu.yazd.ac.ir Graduate M.Sc
2 S.Mojtaba Matinkhah matinkhah@yazd.ac.ir Assistant Professor PhD
3 Mohammad Ghasemzadeh m.ghasemzadeh@yazd.ac.ir Associate Professor PhD

Abstract

Unmanned Aerial vehicles (UAVs) emerged into a promising research trend applied in several disciplines based on the benefits, including efficient communication, on-time search, and rescue operations, appreciate customer deliveries among more. The current technologies are using fixed base stations (BS) to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. These open gates for the UAVs technology to be used as a mobile alternative to increase accessibility in beam selection with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. This paper presents a first fast semi-online 3-Dimensional machine learning algorithm suitable for proper beam selection as is emitted from UAVs. Secondly, it presents a detailed step by step approach that is involved in the multi-armed bandit approach in solving UAV solving selection exploration to exploitation dilemmas. The obtained results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any mobile networked devices issue based on bandit samples like Thompson sampling, Bayesian algorithm, and ε-Greedy Algorithm. The results further illustrated that the 3-Dimensional algorithm optimizes utilization of technological resources compared to the existing single and the 2-Dimensional algorithms thus close optimal performance on the average period through machine learning of realistic UAV communication situations.