Machine Learning-Based Multi-UAV Deployment for Uplink Traffic Sizing and Offloading in Cellular Networks

Traffic offloading in cellular networks is considered an evolving application of unmanned aerial vehicles (UAVs). UAVs have attractive characteristics for this application, such as the ease of deployment, the relatively low cost and the line-of-sight signal propagation. This paper proposes a machine learning-based deployment of UAVs as temporary base stations (BSs) to complement cellular communication systems in times of excess traffic loads. In this role, the UAV is tasked with the proper sizing of the excess mixed traffic demands on the terrestrial BSs and the subsequent offloading of this traffic, given its different QoS requirements. We achieve this objective by optimizing the number of needed UAVs and their three-dimensional (3D) positions. A traffic estimation technique based on the autoregressive integrated moving average (ARIMA) model is utilized to estimate the mixed traffic demand. Our proposed machine-learning approach, based on the reinforcement learning (RL) methodology, aims to obtain real-time results close to the solution's optimal bound. Simulation results show that the proposed RL solution achieves its close-to-optimal real-time objectives. The proposed UAV deployment approach is also shown to clearly outperform a commonly used generic technique for UAVs deployment in such situations.