Unified 2D and 3D radio map construction supports network planning, wireless digital twins, and unmanned aerial vehicle (UAV) applications. In urban environments, blockage, reflection, and diffraction make accurate construction expensive for physics-based solvers. Autoregressive next-token prediction offers a single sequential formulation that can cover both 2D and 3D generation, but standard raster ordering ignores the spatial structure of radio propagation. When generation follows propagation, each token is predicted from propagation-relevant history rather than spatially arbitrary context, which provides more causally informative conditioning and lowers conditional uncertainty. We propose PILOT, a pretrained autoregressive framework that replaces raster scan with a wavefront sequence expanding outward from the transmitter. Each prediction step is guided by an environment-aware instruction that spatially aligns environment features with the queried radio map region. The same framework extends to 3D radio maps through height-slice stacking while a gradient loss enforces vertical continuity. On standard 2D benchmarks, PILOT achieves the lowest NMSE among all baselines. For volumetric generation, it reduces NMSE by 78% relative to the diffusion baseline at roughly $2500\times$ faster inference. It also outperforms methods that rely on 10% sparse measurements and achieves the best zero-shot results in the cross-domain evaluation.

翻译：统一的二维与三维无线电地图构建支持网络规划、无线数字孪生及无人机应用。在城市环境中，阻挡、反射和衍射问题使得基于物理的求解器难以低成本实现精确构建。自回归下一个词元预测提供了可同时覆盖二维与三维生成的单一序列化范式，但标准光栅排序忽视了无线电传播的空间结构。当生成过程遵循传播规律时，每个词元的预测基于传播相关历史而非空间任意上下文，这提供了更具因果信息性的条件约束并降低了条件不确定性。我们提出PILOT——一种预训练自回归框架，采用从发射器向外扩展的波前序列替代光栅扫描。每个预测步骤由环境感知指令引导，该指令将环境特征与查询的无线电地图区域进行空间对齐。通过高度层堆叠，同一框架可扩展至三维无线电地图，同时梯度损失函数确保垂直连续性。在标准二维基准测试中，PILOT在所有基线方法中实现了最低的归一化均方误差。对于体积生成，其相对于扩散基线降低了78%的归一化均方误差，推断速度提升约2500倍。该方法还优于依赖10%稀疏测量的方法，并在跨域评估中取得了最佳零样本结果。