Cellular Connected UAV Anti-Interference Path Planning Based on PDS-DDPG and TOPEM

Abstract

Due to the randomness of channel fading, communication devices, and malicious interference sources, uncrewed aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path-planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design, in this article. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this article, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and double deep Q-network learning (DDQN) algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50 %, significantly improves convergence rate, and reduces the episode required for convergence to 20 %. It can alsohelp UAVs choose better paths than basic DDPG and DDQN algorithms.