A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications. (1st August 2020)

• Record Type: Journal Article
• Authors: Meng, Hao; Shafik, Wasswa; Matinkhah, S. Mojtaba; Ahmad, Zubair
• Other Names: Lagkas Thomas Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-08-01
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/1428968
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε -greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.