🤖 AI Summary
To address the challenge of interference-resilient communication for large-scale aerial reconfigurable intelligent surfaces (ARIS) under adaptive jamming, this paper proposes a continuous robust transmission framework based on mean-field modeling. To overcome the high computational complexity and poor scalability of conventional discrete optimization, we model ARIS deployment as a continuous spatial density function and jointly optimize base station beamforming, ARIS reflection coefficients, and spatial density distribution via variational optimization and Riemannian manifold methods. Theoretically, we characterize a fundamental trade-off between jammer proximity and directionality, and introduce a spatial water-filling principle to guide optimal ARIS resource allocation. Simulation results demonstrate that the proposed framework significantly improves sum rate, achieves computational complexity independent of the number of UAVs, and exhibits strong robustness and scalability—establishing a novel paradigm for large-scale, interference-resilient ARIS deployment.
📝 Abstract
Aerial reconfigurable intelligent surfaces (ARIS), deployed on unmanned aerial vehicles (UAVs), could enhance anti-jamming communication performance by dynamically configuring channel conditions and establishing reliable air-ground links. However, large-scale ARIS faces critical deployment challenges due to the prohibitive computational complexity of conventional discrete optimization methods and sophisticated jamming threats. In this paper, we introduce a mean field modeling approach to design the spatial configuration of ARIS by a continuous density function, thus bypassing high-dimensional combinatorial optimization. We consider an adaptive jammer which adjusts its position and beamforming to minimize the sum-rate. A key finding reveals that the jammer's optimal strategy is governed by a proximity-directivity trade-off between reducing path loss and enhancing spatial focusing. To combat the jamming, we propose a robust anti-jamming transmission framework that jointly optimizes the BS beamforming, the ARIS reflection, and the ARIS spatial distribution to maximize the worst-case sum-rate. By leveraging variational optimization and Riemannian manifold methods, we efficiently solve the functional optimization problems. Our analysis further unveils that the optimal ARIS deployment follows a spatial water-filling principle, concentrating resources in high-gain regions while avoiding interference-prone areas. Simulation results demonstrate that the proposed framework remarkably improves the sum-rate. Furthermore, the computational complexity of the proposed algorithm is independent of the number of UAVs, validating its effectiveness for scalable ARIS-assisted anti-jamming communications.