Energy-aware Trajectory Optimization for UAV-mounted RIS and Full-duplex Relay

TL;DR

This work tackles energy-constrained UAV trajectory optimization for UAV-mounted RIS and UAV-mounted FDR systems in TDMA uplink. It develops dedicated energy-consumption models that account for weight, battery, and on-board processing, and jointly optimizes TDMA scheduling and 3D UAV trajectories using alternating optimization and successive convex approximation. The study reveals a nuanced trade-off: increasing RIS reflection elements boosts per-user rates but also weight-driven energy costs, while UAV-mounted FDRs consistently outperform RIS in the studied setting due to more favorable energy and path-loss characteristics; battery capacity further shapes optimal trajectories by enabling longer, more effective flight paths. The results underscore the practical importance of energy-aware design in UAV-assisted networks, guiding when to deploy RIS versus FDR and how to size components and plan trajectories for sustained, fair network performance in 6G scenarios.

Abstract

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode and forward (DF) protocol, aiming to maximize the network minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

Figures (7)

• Figure 1: UAV-assisted network topology.
• Figure 2: Benchmark UAV trajectories: (a) Circle (b) Rombus (c) Spiral.
• Figure 3: Minimum rate vs the number of reflecting elements $M$ for $\sigma^2=-144$ dB.
• Figure 4: Minimum rate versus UAV-mounted FDR Antennas for $\sigma^2=-144$ dB
• Figure 5: Minimum rate versus UAV-mounted FDR Antennas for $\sigma^2=-114$ dB and $L=750$ m.

Theorems & Definitions (1)

• Remark 1