AI summaryⓘ
The authors study how to improve wireless signal coverage in low-altitude airspace by using intelligent reflecting surfaces (IRS) alongside traditional base stations (BS) that have fixed downward angles. They create a model to decide the best locations, angles, and signal settings for IRS to boost the weakest signal areas without changing existing base station setups. They also develop mathematical tools to understand how signals spread based on height and distance, and propose an optimization algorithm to find the best IRS configurations within a budget. Their simulations show that this approach improves the worst-case signal quality more than other methods. Overall, the authors provide a way to better cover 3D airspace using smart placement and control of IRS with fixed BSs.
Base Station (BS)Intelligent Reflecting Surface (IRS)Low-altitude coverageSignal-to-noise ratio (SNR)Downtilt3D wireless channel modelArray gainPhase shift optimizationMixed-integer optimizationAlternating optimization (AO)
Authors
Guoying Zhang, Qingqing Wu, Ailing Zheng, Xingxiang Peng, Wen Chen, Wei Feng
Abstract
Terrestrial base stations (BSs) are typically configured with fixed downtilt to serve ground users, resulting in weak illumination of low-altitude airspace even under line-of-sight (LoS) propagation. In this paper, we establish a channel model that incorporates BS and intelligent reflecting surface (IRS) radiation patterns for three-dimensional (3D) low-altitude coverage while preserving the existing BS configuration. We formulate a budget-constrained IRS deployment problem that jointly determines candidate-site selection, IRS orientations, and phase shifts to maximize the worst-case signal-to-noise ratio (SNR) over the 3D low-altitude airspace. The selected sites and optimized IRS parameters remain fixed after deployment, yielding a quasi-static IRS configuration. We characterize the illumination geometry between the fixed-downtilt BS and rooftop candidates by deriving the nonnegative installation-height range satisfying the BS main-lobe condition. The separation between the mapped main-lobe height boundaries grows linearly with horizontal BS-to-site distance and decreases inversely with the number of BS antennas. We further derive an analytical lower bound on the regional worst-case normalized array gain achievable through IRS phase design over served directions with different direction spans. The resulting sufficient direction span decreases inversely with the square root of the number of IRS elements when the same worst-case normalized gain guarantee is maintained. We develop a mixed-integer alternating optimization (AO) algorithm to solve the resulting problem. Simulation results validate the analytical characterizations and show that the proposed scheme achieves higher worst-case SNR than benchmarks across different deployment budgets.