Joint Airborne Wireless Positioning and Communication Services With Pattern Division Multiple Access (PDMA)

Unmanned Aerial Vehicles (UAVs) have recently emerged as a key component for various 5G applications, offering either precise positioning or high-speed, low-latency data communication services. However, delivering both wireless positioning and data communication at the same time using UAVs remains challenging due to the different requirements of each service. While data communication demands high throughput, positioning services necessitate the establishment of multiple connections simultaneously, but achieving high throughput for all the simultaneous connections remains challenging. Pattern Division Multiple Access (PDMA), which flexibly shares Resource Elements (REs) among multiple users, presents a promising solution to this issue. In this study, we investigate the joint provisioning of communication and positioning services in a UAV airborne network using PDMA. We propose a comprehensive approach that addresses the joint problem of user-to-UAV association, RE allocation, and transmission power control. Our goal is to enhance the precision of positioning services while satisfying communication constraints. To tackle this problem, we develop both an exact solution and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, tailored for this highly complex non-convex optimization problem. Extensive simulations demonstrate that our attentional MADDPG algorithm achieves higher positioning accuracy compared to state-of-the-art solutions and can efficiently address interferences, thereby improving both services.