UAV-enabled Adaptive Radio Sampling for High-quality Radio Environment Map Construction in Unknown Environments

Radio environment maps (REMs) integrate multidomain environmental data from real-world wireless scenarios, of which the high-quality construction is crucial for optimizing wireless communication and ensuring efficient spectrum utilization. Owing to the high mobility and flexibility, UAV-enabled radio sampling is highly effective for REM construction by informative path planning in information-rich positions. However, existing UAV sampling strategies rely solely on Gaussian process (GP) uncertainty reduction, which induces structural inaccuracies in REMs, particularly in environments with unknown obstacles. To address this issue, we first introduce a dual-information metric that combines uncertainty reduction with signal variation magnitude. Then we formulate an adaptive path planning problem for radio sampling under the energy constraint. Considering the NP-hardness of this problem, we propose a deep reinforcement learning-based adaptive sampling for UAV, termed DRL-ASU. To be specific, we propose an obstacle-aware probabilistic roadmap (PRM) generation mechanism that transforms the initial problem into a sequential graph-based decision problem. On this basis, we design an attention-based policy model for efficient resolution, optimized via multi-environment proximal policy optimization (PPO) training, which enables robust cross-scenario adaptability in diverse radio environments. Simulation results demonstrate that DRL-ASU achieves a 5.39% reduction in root mean square error (RMSE) and a 10.14% improvement in structural similarity index measure (SSIM) compared to single-metric baselines. Furthermore, we conduct real-world experiments to demonstrate the generalizability of our proposed DRL-ASU scheme.