Determining the 3D Positions of Each UAV based on a Distributed Implementation of Q-Learning
G. L. Sravanthi1, Vasumathi Devi Majeti2, Ashok Kumar Nanduri3, I. V. Haritha4
1G. L. Sravanthi, Assistant Professor, Vignan’s Nirula Institute of Technology and science for women, India.
2Vasumathi Devi Majeti, Assistant Professor, Vignan’s Nirula Institute of Technology and science for women, India.
3Ashok Kumar Nanduri, Assistant Professor, Vignan’s Nirula Institute of Technology and science for women, India.
4I. V. Haritha, Assistant Professor, MLR Institute of Technology, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1409-1415 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1226109119/2019©BEIESP | DOI: 10.35940/ijeat.A1226.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main goal this paper is to provide a circulated as well as intelligent treatment to the complication of setting up many UAV-BSs so as to maximize the variety of covered individuals in an unexpected emergency circumstance situation. This problem is actually of high significance in emergency situations, looking at that the fastest a communication network may be set up, a lot more human way of lives can be saved. This optimization worry similarly installs a daunting obstacle, as a result of the contrasting health conditions of the environment, like consumers relocating along with a variety of rates, individuals possessing various criteria as well as additionally the UAV-BSs being limited in both RAN as well as likewise backhaul sources. This paper will certainly create the 3d environments of each UAV located upon a circulated execution of q-learning. Specifically, the important UAV troubles like three-dimensional launch, performance evaluation, system modeling, and power functionality are found along with depictive end results.
Keywords: Unmanned Aerial vehicles(UAVs), Self organizing networks, Machine Learning.