Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks.

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks.

Evaluating the Quality of Experience Performance Metric for UAV-Based Networks.

In this work, we consider a UAV-assisted cell in a single user scenario. We consider the Quality of Experience (QoE) performance metric calculating it as a function of the packet loss ratio. In order to acquire this metric, a radio-channel emulation system was developed and tested under different conditions.