Unmanned Aerial vehicles Analysis to Social Networks Performance

Unmanned Aerial vehicles Analysis to Social Networks Performance

Wasswa Shafik, S. Mojtaba Matinkhah


The innovation of unmanned aerial vehicles (UAVs) is a promising research area where current communications, social data sharing, digital immigrations, when the base station is fixed, it is associated with some problems like inaccessibility in remote areas. This leads to an attempt for the drone technology to be used as a mobile base station to increase accessibility with a fifth generation (5G) connectivity that is 99.999% availability increasing social connectivity. In this paper, we analyze how the armed bandit solves social networks problems in particular UAVs. For their autonomous operation, the ability to explore unmapped areas is imperative. We present a description of bandit problems and give some applicability to address the 5G UAVs in the drone system to achieve close optimal performance. The simulated results in MATLAB depicted that the multi-armed bandit problem can be applied in optimizing the performance of any network issue the returned solution as a function of the number of function evaluations and its fast applicability in data gathering tasks, search, and rescue among others. The results obtained further shown the optimized beam selection within environmental awareness in the mm-Wave drone system to achieve close optimal performance on the average period through learning from the realistic situation. 


Drones, Beamforming, Social Network, Unmanned Aerial Vehicles


  • [1] H. Meng, W. Shafik, S. M. Matinkhah, and Z. Ahmad, "A 5g beam selection machine learning algorithm for unmanned aerial vehicle applications," Wireless Communications and Mobile Computing, 2020.