DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables network operators ensure their customer’s a better quality service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are significant part the due continuously growing use in various applications. For coverage, cost-effective, connectivity provisioning, UAVs have emerged as best choice for telco operators. can be used flying base stations, edge servers, relay nodes networks. On other side, Multi-access Edge Computing (MEC) technology 5G experience (QoE) users with different QoS requirements. However, coverage enhancement face several challenges such trajectory designing, path planning, optimization, assurance, mobility management, etc. efficient proactive planning optimization highly dynamic environment containing buildings obstacles challenging. So, an automated Artificial Intelligence (AI) enabled QoS-aware solution needed optimization. Therefore, this work introduces well-designed AI MEC-enabled architecture UAVs-assisted network. has Deep Reinforcement Learning (DRL) algorithm real-time fulfills provisioning. A greedy-policy approach maximize long-term reward serving more QoS. Simulation results reveal superiority proposed DRL mechanism energy-efficient over existing models.