Geometry Based UAV Trajectory Planning for Mixed User Traffic in mmWave Communication

TL;DR

This work addresses UAV-based trajectory planning for mixed-traffic mmWave A2G networks with mobility and obstacles. It pioneers a geometry-driven two-stage approach that first uses the Apollonius circle ($C_a$) and the minimum enclosing ball of balls ($MB$) to identify a URLLC-friendly zone, then selects a final UAV position within that zone to maximize eMBB throughput, leveraging both LoS and NLoS links where appropriate. The method explicitly prioritizes URLLC coverage (ensuring reliability and latency) before optimizing overall throughput for eMBB, and provides a complexity-aware framework with discretized candidate regions. Results show a notable improvement in the sum throughput of URLLC and eMBB users compared to a benchmark, validating the approach's effectiveness under mobility and obstacle constraints.

Abstract

Unmanned aerial vehicle (UAV) assisted communication is a revolutionary technology that has been recently presented as a potential candidate for beyond fifth-generation millimeter wave (mmWave) communications. Although mmWaves can offer a notably high data rate, their high penetration and propagation losses mean that line of sight (LoS) is necessary for effective communication. Due to the presence of obstacles and user mobility, UAV trajectory planning plays a crucial role in improving system performance. In this work, we propose a novel computational geometry-based trajectory planning scheme by considering the user mobility, the priority of the delay sensitive ultra-reliable low-latency communications (URLLC) and the high throughput requirements of the enhanced mobile broadband (eMBB) traffic. Specifically, we use geometric tools like Apollonius circle and minimum enclosing ball of balls to find the optimal position of the UAV that supports uninterrupted connections to the URLLC users and maximizes the aggregate throughput of the eMBB users. Finally, the numerical results demonstrate the benefits of the suggested approach over an existing state of the art benchmark scheme in terms of sum throughput obtained by URLLC and eMBB users.

Figures (3)

• Figure 1: Communication Environment
• Figure 2: Candidate zone for URLLC traffic
• Figure 3: Impact of (a) Coverage radius of UAV, (b) Number of obstacles, and (c) User velocity on Sum throughput.

Theorems & Definitions (2)

• Definition 1
• Definition 2