Orientation-Aware Control and Trajectory Design for Aerial RIS-Assisted Wireless Communications

Abdoul Karim A. H. Saliah; Hajar El Hammouti; Daniel Bonilla Licea; Giuseppe Silano

cs.NISep 19, 2026

Orientation-Aware Control and Trajectory Design for Aerial RIS-Assisted Wireless Communications

Authors: Abdoul Karim A. H. Saliah, Hajar El Hammouti, Daniel Bonilla Licea, Giuseppe Silano

Organizations: College of Computing, Mohammed VI Polytechnic University, Ben Guerir, Morocco · Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic

Abstract

Aerial Reconfigurable Intelligent Surfaces (ARISs) can enhance wireless connectivity in obstructed environments by combining reconfigurable metasurfaces with aerial mobility. However, in practical deployments, the Reconfigurable Intelligent Surface (RIS) is rigidly mounted on a multi-rotor Unmanned Aerial Vehicle (UAV), which creates coupled position-orientation dynamics that directly affect communication quality. Since RIS performance is highly sensitive to orientation, point-mass or orientation-invariant models may lead to optimistic performance estimates. This paper proposes an orientation-aware ARIS framework that explicitly accounts for UAV dynamics, actuation limits, and orientation-dependent channels. The proposed approach combines a communications-guided reference trajectory planner with a Nonlinear Model Predictive Control for dynamically feasible trajectory tracking and favorable RIS orientation, while employing lightweight beamforming and RIS phase-shift updates. Simulations show that accounting for orientation improves user throughput by 13.77%, while the proposed joint communication updates improve network throughput by 28% over phase-shift-only optimization and by at least a factor of 2.5 over beamforming-only optimization. These results highlight the importance of orientation-aware modeling and joint communication-control design in ARIS-assisted wireless systems.

Figures & tables

Fig. 1: ARIS-assisted communication system architecture: a RIS rigidly mounted on a multi-rotor UAV relays signals between the base station and ground users. The UAV trajectory is expressed in the world (inertial) reference frame $\mathcal{F}_{W}$ , and a RIS orientation in the UAV body-fixed frame $\mathcal{F}_{B}$ .

Communication
Notation	Description
$\mathcal{I},\mathcal{N},\mathcal{M}$	User, BS-antenna, and RIS-element index sets
$I,N,M$	Number of users, BS antennas, and RIS elements
$\mathbf{H},\mathbf{g}_{i},\boldsymbol{\Theta}$	BS-RIS and RIS-user $i$ channels, and phase-shift
$\mathbf{p}^{\text{BS}},\mathbf{p}_{i},\mathbf{p}^{\text{v}}$	Position of BS, of user $i$ , and of a virtual user located at the barycenter of the users’ positions
$\mathbf{f},G^{\mathrm{ap}}_{i}$	BS beamforming vector and RIS aperture gain of $i$

TABLE I: Notation List

Fig. 2: Geometric model of the ARIS-assisted communication system with orientation-aware coordinate frames and signal directions.

Fig. 3: Block diagram of the proposed sub-optimal control strategy.

Parameter	Value	Unit
UAV Dynamic Model
Mass	$m=1.042$	kg
Gravity	$g=9.81$	m/s 2
Arm length	$d=0.23$	m
Inertia matrix	$J=\mathrm{diag}(0.015,0.015,0.070)$	kg $\cdot$ m 2
Thrust coefficient	$b_{f}=5.95\times 10^{-4}$	N/Hz 2

TABLE II: UAV Dynamic Model Parameters and NMPC Weights

Fig. 4: Reference trajectory over the communication-aware cost map, showing the selected path toward favorable regions.

Fig. 5: 3D UAV trajectory tracking, where curves represent reference, HoT, and NoT paths, illustrating overall tracking behavior.

Fig. 6: UAV position components over time for HoT and NoT, showing tracking performance and altitude variations.

Fig. 7: Comparison of the proposed PURG trajectory with the PSCA-RG benchmark in the 2D spatial plane.

Fig. 8: Normalized cumulative sum-rate achieved by the reference trajectories generated by PURG and PSCA-RG over the mission duration. The curves are normalized by the maximum value attained by the PURG scheme.

Fig. 9: Sensitivity analysis of the PURG trajectory for different values of the proxy utility exponent $\gamma$ compared to the RRG trajectory.

Fig. 10: Normalized cumulative sum-rate for different $\gamma$ values, showing the convergence of PURG toward the RRG as $\gamma$ increases. All curves are normalized by the maximum cumulative sum-rate attained over all methods.

Fig. 11: Average user data rate versus time, comparing HoT, NoT, and SL schemes, highlighting improved stability and QoS compliance.

Fig. 12: Sum-rate over time for different schemes, showing the benefit of joint optimization and orientation tracking.

Fig. 13: Average cumulative throughput per user over time, illustrating the benefits of orientation tracking and communication-aware trajectory design.

Fig. 14: Cumulative throughput, showing improved throughput for orientation-aware and joint optimization strategies.

Fig. 15: Evolution of the average QoS slack variable over time for the HoT scheme. The QoS slack is averaged over all users.

Fig. 16: Network throughput versus number of RIS elements, demonstrating scalability and performance improvement trends.

Fig. 17: Network throughput versus number of users, highlighting efficiency of the proposed approach.

Fig. 18: Tracking errors over time, comparing HoT and NoT, and illustrating trade-offs between position and orientation tracking.

Fig. 19: UAV velocity profile over time, showing smooth evolution within imposed limits.

Fig. 20: Euler angles over time in the HoT scheme, demonstrating smooth attitude behavior.

Fig. 21: Angular velocities over time, showing bounded behavior.

Fig. 22: Rotor speeds over time, illustrating smooth actuation within physical limits.

Fig. 23: Distribution of the NMPC optimization solve time over all control iterations. The vertical red line denotes the sampling period $T_{s}$ .

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

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This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with stochastic link activation. It models UAV motion and attitude, time-varying Rician angles, and angle-dependent RIS reflection. A joint optimization of UAV trajectory, attitude, and RIS phases is formulated to maximize average sum rate under mobility, energy, and hardware constraints. The problem is addressed using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios. Results demonstrate effective cross-scenario generalization, with zero-shot transfer outperforming direct DRL transfer and online fine-tuning achieving competitive performance with fewer interactions.

Abstract

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