A Fast Machine Learning for ۵G Beam S election for Unmanned Aerial Vehicle Applications

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 (۵G) connectivity that focuses on increased availability and connectivity. This paper presents a first fast semi-online ۳-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 ۳-Dimensional algorithm optimizes utilization of technological resources compared to the existing single and the ۲-Dimensional algorithms thus close optimal performance on the average period through machine learning of realistic UAV communication situations.

Keywords:

Unmanned Ariel Vehicle , Multi-Armed Bandit , Reinforcement Learning Algorithms , Beam selection