arXiv subjects

Wenlihan Lu

Publications and source records attributed to Wenlihan Lu.

6 recordsLinked to original sources

arXiv2026

TeRFS: Temporal-Evolving Radio Field Synthesis

While radio-frequency (RF) field synthesis is fundamental to wireless networking, current approaches remain constrained by static assumptions, leaving them unable to track the rapid multipath reorganization of dynamic scenes. Modeling these transitions requires addressing two coupled challenges: explicit temporal representation and the capture of discrete path lifecycles. To bridge this gap, Temporal-Evolving Radio Field Synthesis (TeRFS) is introduced. TeRFS utilizes an anisotropic spherical Gaussian (ASG) directional basis to represent sparse, sharp angular structures, bound to analytical temporal envelopes that regulate path lifecycles. This formulation induces a mathematical birth-and-death mechanism, enabling individual multipath trajectories to emerge and vanish with temporal precision, a capability beyond the reach of standard smooth interpolation. Evaluations demonstrate that TeRFS outperforms state-of-the-art (SOTA) baselines, achieving an 11.5% reduction in mean squared error (MSE) alongside a 6.9 times training speedup. Even in environments characterized by extreme structural mutation, TeRFS maintains robust tracking of dynamic reorganizations, limiting median absolute error to 1.52 dB and establishing its utility for high-mobility wireless applications. The dataset and code is available at https://github.com/zmydsg/TeRFS.

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arXiv2026

FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking

Precise aerial radio environment characterization is vital for low-altitude airspace planning. However, existing datasets and construction methods lack the high-resolution granularity required for complex aerial spaces, particularly failing to capture spatial variations across both horizontal and vertical dimensions. To address these gaps, this paper introduces FARM, a pioneering foundation model for unified aerial radio map (ARM) construction. FARM is supported by our newly curated, high-granularity full-domain ARM dataset, which features multi-band and multi-antenna configurations, effectively filling a critical void in comprehensive low-altitude radio data. Structurally, FARM leverages a masked autoencoder to extract deep latent representations of the aerial radio environment, which subsequently guide a diffusion-based decoder to synthesize high-fidelity signal distributions through only a few iterative refinement steps. Benefiting from this design, the architecture seamlessly accommodates both condition-based and condition-free ARM construction, providing robust support for diverse signal and environmental priors. Extensive experiments demonstrate that FARM significantly outperforms state-of-the-art benchmarks while exhibiting strong cross-scenario generalization. Crucially, we validate the transferability of FARM on a real-world dataset collected from field tests, proving its robust deployment capability. Ultimately, FARM serves as a foundational infrastructure for the low-altitude economy by enabling autonomous aerial logistics and intelligent urban networking.

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arXiv2026

Active Perception for Radio Map Reconstruction in Uncharted 3D Air-Ground Environments

Radio maps provide the essential foundation for low altitude networking systems. Unlike terrestrial radio maps that are typically generated via drive test measurements, mapping the air-ground environment requires the deployment of unmanned aerial vehicles (UAVs). This shift introduces two formidable challenges in uncharted 3D scenarios. First, sparse radio measurements and incomplete geometric observations hinder accurate reconstruction. Second, the large 3D action space and strict power constraints from high spectrum scanner energy consumption make informative exploration difficult. To address these issues, this paper proposes 3D uncertainty aware radio active mapping (3D-URAM), a closed loop active perception framework that decouples the mapping process into two offline trained stages. In Stage I, a Bayesian UNet is developed to recover radio maps from sparse measurements and partial geometry while providing calibrated predictive uncertainty. In Stage II, a dynamic probabilistic roadmap and a transformer based waypoint selection policy trained via proximal policy optimization maximize long horizon uncertainty reduction under travel budgets. Experimental results demonstrate that 3D-URAM reduces reconstruction error by over 50% compared to representative baselines. Real-world field tests within a 300mx200mx100m space also validate the potential of active radio map reconstruction.

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arXiv2026

Transfer to Sky: Unveil Low-Altitude Route-Level Radio Maps via Ground Crowdsourced Data

The expansion of the low-altitude economy is contingent on reliable cellular connectivity for unmanned aerial vehicles (UAVs). A key challenge in pre-flight planning is predicting communication link quality along proposed and pre-defined routes, a task hampered by sparse measurements that render existing radio map methods ineffective. This paper introduces a transfer learning framework for high-fidelity route-level radio map prediction. Our key insight is to leverage abundant crowdsourced ground signals as auxiliary supervision. To bridge the significant domain gap between ground and aerial data and address spatial sparsity, our framework learns general propagation priors from simulation, performs adversarial alignment of the feature spaces, and is fine-tuned on limited real UAV measurements. Extensive experiments on a real-world dataset from Meituan show that our method achieves over 50% higher accuracy in predicting Route RSRP compared to state-of-the-art baselines.

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arXiv2025

Relaying Signal When Monitoring Traffic: Double Use of Aerial Vehicles Towards Intelligent Low-Altitude Networking

In intelligent low-altitude networks, integrating monitoring tasks into communication unmanned aerial vehicles (UAVs) can consume resources and increase handoff latency for communication links. To address this challenge, we propose a strategy that enables a "double use" of UAVs, unifying the monitoring and relay handoff functions into a single, efficient process. Our scheme, guided by an integrated sensing and communication framework, coordinates these multi-role UAVs through a proactive handoff network that fuses multi-view sensory data from aerial and ground vehicles. A lightweight vehicle inspection module and a two-stage training procedure are developed to ensure monitoring accuracy and collaborative efficiency. Simulation results demonstrate the effectiveness of this integrated approach: it reduces communication outage probability by nearly 10% at a 200 Mbps requirement without compromising monitoring performance and maintains high resilience (86% achievable rate) even in the absence of multiple UAVs, outperforming traditional ground-based handoff schemes. Our code is available at the https://github.com/Jiahui-L/UAP.

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arXiv2025

Bayesian-Driven Graph Reasoning for Active Radio Map Construction

With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commonly deployed for data collection using waypoint-based navigation; however, their limited battery capacity significantly constrains coverage and efficiency. To address this, we propose an uncertainty-aware radio map (URAM) reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation. Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning over a probabilistic roadmap, using uncertainty estimates to plan informative and energy-efficient trajectories. This graph-based reasoning enables intelligent, non-myopic trajectory planning, guiding agents toward the most informative regions while satisfying safety constraints. Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.

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