Joint UAV-RIS Deployment and Resource Optimization for Near-Field SWIPT: A Blind-Start Integrated Framework

Abstract

This paper proposes an integrated two-phase framework for deploying unmanned aerial vehicle-mounted reconfigurable intelligent surfaces (UAV-RIS) in near-field simultaneous wireless information and power transfer (SWIPT) systems, subject to the practical constraint of unknown user and UAV-RIS positions. To overcome the joint challenges of blind access, near-field spherical-wave effects, and high-dimensional optimization, we first design a beam scanning and UAV wireless power transfer (WPT) phase, which employs a near-field multi-beam scanning scheme with orthogonal Hadamard sequences for coarse three-dimensional positioning and device identification, followed by robust wide-beam WPT to the UAV-RIS. Second, we propose a coupled optimization phase that jointly addresses positioning, channel estimation, and SWIPT. This core phase innovatively synchronizes UAV-RIS placement optimization with cascaded channel estimation, formalized by three dependency theorems, and then performs joint design of active beamforming, RIS phase shifts, and power allocation via an efficient alternating optimization algorithm. Extensive simulations demonstrate that the proposed framework reduces positioning error by 72%-78%, lowers channel estimation NMSE by 91.9% at low-to-medium signal-to-noise ratios, and achieves an 8.8% gain in spectral efficiency over non-RIS systems, while converging within 3-5 iterations. This work establishes a systematic methodology for practical UAV-RIS-enhanced near-field SWIPT deployment starting from complete positional uncertainty.