Joint Positioning and Data Communication Provisioning Using 5G Pattern Division Multiple Access (PDMA) Airborne

Using UAVs (Unmanned Aerial Vehicles) to provide either precise positioning or high speed, low latency data communication service has recently emerged for a new class of 5G applications. However, the joint optimization of data communication and wireless positioning in a UAV airborne network is challenging because each service has different requirements. Communication services demand high throughput, while positioning services require the establishment of multiple connections simultaneously. Pattern Division Multiple Access (PDMA) is an innovative communication technique based on Non-orthogonal Multiple Access (NOMA), which facilitates the sharing of REs (Resource Elements) among multiple users. In this paper, we formulate the joint problem of user-to-UAV association, RE allocation, and transmission power control, aimed to improve the precision of positioning service while meeting the communication constraints. We propose both exact and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to solve this non-convex optimization. In addition, to address the problem of interference among users, we propose an attentional Multi-agent Deep Deterministic Policy Gradient (MADDPG) approach. Extensive simulations demonstrated that our proposed algorithm can achieve higher positioning accuracy than state-of-the-art solutions while serving more users.