Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming (1908.04082v3)

Abstract: Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

• The paper introduces a joint optimization method that integrates UAV trajectory design with RIS passive beamforming to maximize the average achievable rate.
• It utilizes a closed-form phase-shift solution and Successive Convex Approximation to efficiently tackle the non-convexity in the optimization process.
• Numerical results reveal significant performance improvements over benchmark algorithms, underscoring RIS's potential in enhancing urban wireless communications.

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

The paper "Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming" presents a focused investigation on enhancing UAV communication systems through the integration of Reconfigurable Intelligent Surfaces (RIS). By jointly optimizing UAV trajectory and RIS passive beamforming, this paper aims to maximize the average achievable rate, a critical measure of system performance.

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly utilized to enhance wireless communications, particularly due to their flexibility and line-of-sight (LoS) transmission capabilities. However, urban environments can impede quality through signal deterioration. The introduction of RIS offers a prospective solution by improving signal propagation through intelligent reflection. This paper addresses the integration of RIS in UAV communication, specifically targeting the maximization of the average achievable rate by designing optimal UAV trajectories and RIS beamforming.

Methodology

The core challenge tackled by the authors is the inherent non-convexity in the joint design problem, which they address by dividing it into subproblems: passive beamforming and trajectory optimization. The passive beamforming subproblem is solved by deriving a closed-form phase-shift solution using phase alignment techniques. For the trajectory optimization, a suboptimal solution is obtained through Successive Convex Approximation (SCA).

Numerical Results

Significant numerical results illustrate the efficacy of the proposed approach. The methodology shows superior performance in maximizing the average achievable rate compared to other benchmark algorithms. Notably, the integration of RIS with optimized UAV trajectories provides a notable uplift in system performance metrics.

Implications and Future Work

The paper's findings bear substantial implications for the design of future 5G and beyond communication networks, where RIS can play a pivotal role in overcoming environmental challenges in urban areas. The successful application of the joint optimization strategy highlights the potential for RIS in dynamic network scenarios, such as public events or disaster recovery scenarios where swift deployment and efficient communication are essential.

Future research could extend this work by considering more complex models, such as networks with multiple UAVs or RIS installations. Additionally, the exploration of real-world deployment scenarios and hardware constraints could offer further insights into the practical implementation of RIS-assisted UAV communications.

Conclusion

This paper provides a rigorous foundational approach to integrating RIS in UAV-based communication systems, showcasing how intelligent surfaces can supplement UAV flexibility to enhance communication performance. The collaborative design of UAV trajectory and RIS beamforming emerges as a promising avenue for future communication networks, setting a groundwork for advanced research in this evolving field.