Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (1805.07182v1)

Abstract: Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main paradigms in cellular UAV communications, i.e., cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location, during which it needs to maintain reliable communication with the cellular network by associating with one GBS at each time instant. We aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target. To tackle this challenging non-convex problem, we first propose a graph connectivity based method to verify its feasibility. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging graph theory and convex optimization techniques. The proposed methods are analytically shown to be capable of achieving a flexible trade-off between complexity and performance, and yielding a solution that is arbitrarily close to the optimal solution in polynomial time. Finally, we make concluding remarks and point out some promising directions for future work.

• The paper introduces a graph-based method to optimize UAV trajectories while ensuring continuous cellular connectivity via nearest base station association.
• It transforms a challenging non-convex trajectory problem into tractable shortest-path problems using graph theory and convex optimization techniques.
• Numerical results demonstrate significant mission time reductions and improved connectivity performance compared to straight-line benchmark trajectories.

Analysis of Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Approach

The integration of unmanned aerial vehicles (UAVs) into cellular networks has evolved as a promising technology aimed at enhancing the communication efficiency for both UAVs and terrestrial users. The paper by Zhang, Zeng, and Zhang offers a comprehensive exploration of the paradigms within cellular UAV communications, focusing particularly on "cellular-enabled UAV communication." The primary objective in this paradigm is to optimize UAV trajectories under communication connectivity constraints imposed by ground base stations (GBSs).

Problem Formulation and Solution Strategy

The core challenge addressed in this paper is the trajectory optimization problem for a UAV tasked with traveling from a specified initial to a final location while ensuring constant link reliability with GBSs. The UAV must establish communication with the nearest available GBS, maintaining a minimal signal-to-noise ratio (SNR). To minimize the mission duration, the authors involve non-convex optimization to design UAV trajectories, adhering to connectivity constraints.

The paper introduces a graph-based method to assess the problem’s feasibility, examining the potential connectivity between vertices on an equivalent graphical representation. By determining the structural properties of optimal UAV trajectories, the authors propose methods leveraging graph theory and convex optimization techniques to derive high-quality approximate solutions. These methods provide flexibility in balancing computational complexity with performance, offering solutions that approach the optimal trajectory within polynomial time.

Methodological Insights

1. Feasibility Verification: The complexity of determining feasible solutions arises from the non-convex nature of the problem. The authors utilize graph connectivity to transform the trajectory design problem into a more tractable format, checking feasibility through connectivity verification on a constructed graph.
2. Optimal Trajectory Design: The trajectory formulation process entails finding the sequence of GBSs that ensures continuous connectivity with the UAV. By evaluating GBS-UAV association sequences, the paper delivers structural insights into optimized flight paths via connected line segments at maximum UAV speed.
3. Graph-Based Optimization: The innovative application of graph theory transforms the problem into shortest-path problems. Two graph construction methods facilitate efficient solution finding, significantly reducing the problem's continuous variables to discrete sets related to GBS associations and UAV handover points.

Numerical Validation and Performance

Empirical results substantiate the proposed methods, demonstrating substantial improvements over benchmark schemes like simple straight-line trajectories. With increased GBS density, the proposed trajectory optimizations reveal pronounced enhancements in quality-of-connectivity and minimized mission time. The flexibility to adjust between complexity and performance confirms the robustness of the proposed methods, as seen through comparative analysis for different density settings.

Implications and Future Work

The implications of this research are profound, both in theory and practice. The trajectory optimization framework introduced herein paves the way for sophisticated UAV applications requiring reliable and robust cellular connectivity. Additionally, future iterations of this research could explore:

• 3D Trajectory Optimization: Integrating altitude dynamics to enhance connectivity further.
• Multi-GBS Association: Exploiting multiple GBS associations using CoMP (Coordinated Multi-Point) transmission strategies.
• QoS Metrics: Expanding the optimization criteria to encompass various Quality of Service (QoS) requirements under different latency and reliability expectations.
• Integration with Other Technologies: Extending applications to incorporate other UAV network paradigms like UAV-assisted cellular communication or exploring potential with technologies such as edge computing and VANETs (Vehicular Ad Hoc Networks).

The treatment of UAV trajectory optimization under connectivity constraints illustrates a significant advancement in addressing emerging needs within automation and communication infrastructures. This work stands as a notable contribution to optimizing cellular network integration for UAVs, charting courses for further exploration in the domain of intelligent aerial communications.