Energy efficient deployment of multiple UAV mounted base stations: a machine learning-based approach. (27th October 2022) - Mandloi, Dilip; Sharma, Rohit; Arya, Rajeev
This paper presents an energy efficient approach for the deployment of multiple 5G enabled unmanned aerial vehicle-mounted base stations (UmBSs) in the area where existing network infrastructure has got demolished due to a natural disaster. In our proposed approach, K-means, a machine learning-based technique, is used to position UmBSs based upon which their transmit power is optimised by solving a convex optimisation problem under the constraints of maximum transmit power of UmBS, maximum height of UmBS, and channel capacity. To ensure the QoS requirements, the association of UEs and UmBS is subjected to the constraint of minimum signal to interference plus noise ratio (SINR) threshold. The effectiveness of the proposed approach is demonstrated in terms of average SINR, average channel capacity, and the number of users served. The performance of the proposed approach is compared with the two baseline approaches through MATLAB simulations.