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Xiangjian Gao

Publications and source records attributed to Xiangjian Gao.

2 recordsLinked to original sources

Timely Ground-Station Status Delivery via UAV-Assisted Data Collection and Hierarchical RL

We consider a UAV-assisted status-update system in which a rotary-wing unmanned aerial vehicle (UAV) collects updates from multiple sensors, stores the newest update of each sensor, and forwards selected buffered updates to a ground station (GS). We minimize a weighted sum of the time-average GS-side age of information (AoI) and UAV energy consumption over a finite horizon. The optimization is subject to half-duplex operation, sensor and UAV energy-causality constraints, transmission-rate and power limits, and a terminal-location constraint. Because collection changes the information age at the UAV but does not reduce the GS-side AoI until the buffered update is forwarded, mobility, collection, and forwarding decisions are coupled over time. We use a hierarchical AoI-energy actor-critic (HAE-AC) controller in which a coarse-timescale actor selects return-feasible UAV movements, while a deterministic per-slot scheduler constructs feasible collection or forwarding sets and assigns the minimum transmit power required for each scheduled transmission. In a 100-sensor deployment, HAE-AC reduces the average GS-side AoI by about 22% relative to an adaptive staleness-gated benchmark. Compared with forward-upon-collection, selective buffered forwarding reduces the number of downlink bursts by roughly 30% while increasing the number of collections per sensor by more than 23%.

cs.IT↗

Deep Learning-Assisted UAV Localization Framework for Post-Disaster Search and Rescue Missions

The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.

eess.SP↗