Mendeley TY _ JOUR ID - 13981112215138 TI - A Fast Machine Learning for 5G Beam Selection for Unmanned Aerial Vehicle Applications JO - Journal of Information Systems and Telecommunication (JIST) JA - ES LA - en SN - 2322-1437 AU - Shafik Wasswa AU - Ghasemzadeh Mohammad AU - Matinkhah S.Mojtaba AD - Yazd University AD - دانشگاه یزد AD - Yazd University Y1 - 2020 PY - 2020 VL - 28 IS - 7 SP - 262 EP - 277 KW - Unmanned Ariel Vehicle KW - KW - Multi-Armed Bandit KW - KW - Reinforcement Learning Algorithms KW - KW - Beam selection KW - DO - N2 - 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. UR - rimag.ir/en/Article/15432 L1 - rimag.ir/en/Article/Download/15432 ER -