A Fuzzy Logic Approach for Improving Throughput of the UAV-Assisted Wireless Networks

Unmanned aerial vehicle (UAV)-assisted wireless network is envisioned as a dominant network in 6G to cope with sudden surge of data rate demand and to provide flexible data connectivity. This network works as a moving hotspot. Existing UAV deployment techniques suffer from limited throughput and user satisfaction. In this paper, we propose a novel UAV deployment algorithm exploiting the fuzzy c-means clustering to overcome the limitations involved in k-means clustering so that a higher network throughput can be achieved and to ensure a higher user satisfaction. We compare the performance of the proposed UAV deployment algorithm with the performance of the state-of-the-art k-means algorithm. Simulation results show that the proposed method outperforms the k-means algorithm in terms of network throughput, user satisfaction ratio, and consistency in throughput. Up to 9% improvement in the network throughput is obtained due to the proposed method. We see that the network throughput is proportional to the number of UAVs, and more users can be satisfied by the proposed method.