# Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm

Source: [Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm](<https://www.sota2.com/research/paper/6a17cc09dbd7150506f812b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-29T05:00:12.873Z

Authors: Feng Qiu, Zheng Fang, Shuhang Zhang, Kangjun Liu, Longkun Zou, Jing Liu, Ke Chen

Published: 2026-05-27T09:47:23.000Z

## Abstract

Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network optimization. Most current methods assume an independently and identically distributed (i.i.d.) training and testing setting based on random sampling. However, practical UAV measurements are collected sequentially along feasible trajectories, resulting in highly structured and spatially correlated patterns. This mismatch introduces a sampling distribution shift that increases the intrinsic difficulty of spatial field recovery and compromises the generalization of models trained under i.i.d. assumptions. To mitigate this issue, we propose a trajectory-aware training paradigm based on Stochastic-Triggered Trajectory-Based Sampling (ST-TBS), which preserves trajectory continuity while introducing sampling variability. Moreover, from a statistical perspective, we show that trajectory-based sampling reduces spatial diversity and increases information redundancy compared to random sampling. Extensive experiments on the RadioMapSeer and SpectrumNet datasets demonstrate that models trained with random sampling suffer significant performance degradation under trajectory-based observations, with RMSE increasing from 0.0391 to 0.2632 on SpectrumNet. Conversely, our proposed ST-TBS method effectively reduces the RMSE to 0.0571. These results highlight the necessity of aligning training and deployment sampling distributions for reliable RME.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Radio Map Estimation on RadioMapSeer](<https://www.sota2.com/research/sota/radio-map-estimation-on-radiomapseer.md>): Radio Map Estimation; RadioMapSeer; RMSE: 0.0305.

- [Radio Map Estimation on SpectrumNet](<https://www.sota2.com/research/sota/radio-map-estimation-on-spectrumnet.md>): Radio Map Estimation; SpectrumNet; RMSE: 0.0391.

- [Radio Environmental Mapping on RadioMapSeer](<https://www.sota2.com/research/sota/radio-environmental-mapping-on-radiomapseer.md>): Radio Environmental Mapping; RadioMapSeer; RMSE: 0.0305.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.28234>)

[PDF](<https://arxiv.org/pdf/2605.28234.pdf>)