A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.Comment: 14 pages,8 figures, journal pape

Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning

To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network's connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results show the effectiveness of this two-step approach for UAV networks.Comment: to appear in IEEE GLOBECOM 202

UAV Trajectory Optimization in Modern Communication Systems: Advances and Challenges

UAV trajectory optimization in modern communication systems is crucial as many research efforts are recorded in integrationof5GinUAVs.Thishasattractedsignificantattention from wireless communication research community around the world. With the rapid advancement in UAV-assisted communication systems, UAV's trajectory optimization has become important due to intrinsic constraints facing in modern communication systems. Notable research activities have been conducted in the direction of UAV trajectory optimization in different communication setups during last few years. Despite the importance of the topic, there are no extensive reviews available in open literature related to UAV trajectory optimization techniques used in 5G. Thus, this paper provide a comprehensive survey on UAV trajectory optimization techniques used in the open literature and advancement to date, with identified research issues and challenges. This provides a valuable reference and new avenues for the future research in this direction