Trajectory Optimization for Cellular-Enabled UAV with Connectivity and Battery Constraints

In this paper, we address the problem of path planning for a cellular-enabled UAV with connectivity and battery constraints. The UAV's mission is to deliver a payload from an initial point to a final point as soon as possible, while maintaining connectivity with a BS and adhering to the battery constraint. The UAV's battery can be replaced by a fully charged battery at a charging station, which may take some time depending on waiting time. Our key contribution lies in proposing an algorithm that efficiently computes an optimal UAV path in polynomial time. We achieve this by transforming the problem into an equivalent two-level shortest path finding problem over weighted graphs and leveraging graph theoretic approaches. In more detail, we first find an optimal path and speed to travel between each pair of charging stations without replacing the battery, and then find the optimal order of visiting charging stations. To demonstrate the effectiveness of our approach, we compare it with previously proposed algorithms and show that our algorithm outperforms those in terms of both computational complexity and performance. Furthermore, we propose another algorithm that computes the maximum payload weight that the UAV can deliver under the connectivity and battery constraints.Comment: This article was presented in part at the IEEE Vehicular Technology Conference (VTC) 2023-Fal

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