Deep reinforcement learning framework for joint optimization of multi-RAT UAV location and user association in heterogeneous networks

The explosive growth of multimedia and Internet of Thing (IoT) devices has led to a huge increase in data traffic requirements with a reduced power consumption demands in 6G communications. In this work, a ground Multiple Radio Access Technology (Multi-RAT) Heterogeneous Network (HetNet) is considered, which is assisted by multiple UAVs, each carrying Multi-RAT base stations (i.e., LTE and Wi-Fi base stations), to utilize the unlicensed spectrum, and provide an on-demand assistance, more capacity, and coverage for diverse ground devices. A Satisfaction to Energy Ratio (SER) is introduced, which is a ratio between the users' satisfaction according to their requirements, and the UAVs' energy consumption. An iterative framework is proposed to maximize the SER by optimizing the UAVs 3D location and the users association. The proposed framework uses a modified K-means algorithm for initialization, Deep Reinforcement Learning (DRL) to optimize the 3D location of UAVs, and regret learning to optimize the user association. Extensive simulations show an improvement percentage that reaches 13%, 25%, 67%, 71%, 28%, 45% in satisfaction index, downlink data rate, uplink power consumption, outage probability, Jain's fairness index, and framework iterations, respectively. In addition, a comparison between different DRL algorithms, observation scenarios, and training approaches is presented to select the best combination of them in the proposed framework.