Optimal Movable-Antenna Control for Multi-Path Sensing Guided by Prior AoA Statistic (extended version)
2026-08-10 • Information Theory
Information Theory
AI summaryⓘ
The authors address the challenge of sensing multiple signal paths using a movable antenna without requiring a slow, exhaustive scan. They propose a method that uses prior knowledge about signal directions to optimize the antenna's orientation once and then perform just two quick scans. By combining these scans with the prior information, their approach accurately finds the directions and arrival times of signals while reducing time and control effort. This makes multi-path sensing faster and more practical compared to previous methods.
multi-path sensingmovable antennaangle-of-arrival (AoA)time-of-arrival (ToA)Fisher informationmaximum a posteriori (MAP)spatial phasesignal processing6G wirelessmechanical scanning
Authors
Jaehong Kim, Changsheng You, Jihong Park, Seung-Woo Ko
Abstract
Multi-path sensing, which aims to extract the geometric attributes of multiple propagation paths, is expected to be a key functionality of 6G. A movable antenna (MA) can enable this functionality by synthesizing an aperture through mechanical motion. However, existing MA-based sensing methods typically rely on exhaustive scanning over the entire movable region, resulting in significant control overhead and sensing latency, which limit their practicality for agile sensing. To address this challenge, this paper develops a prior-guided agile multi-path sensing framework that leverages weak prior angle-of-arrival (AoA) statistics as side information. The proposed framework is built on two key steps. First, the movable plate's three-dimensional orientation is optimized only once to configure a mechanically feasible scan region that enhances path visibility while preserving inter-path discriminability, guided by Fisher information analysis. Second, given the optimal plate orientation, the MA performs only two linear scans, whose non-collinear spatial phase projections are fused with the prior AoA statistics through a maximum a posteriori (MAP)-based estimator to recover the elevation and azimuth AoAs of multiple paths. The estimated AoAs are subsequently used to extract the times-of-arrival (ToAs) by enhancing the target path component while suppressing interference from other paths. With only one orientation adjustment and two linear scans, the proposed framework enables agile multi-path sensing with significantly reduced control overhead and latency, while achieving AoA and ToA estimation accuracy close to the single-path benchmark.