HAMM-UNet: Height-Adaptive Multi-Modal Radio Map Reconstruction Based on Sparse Measurements

Abstract

Radio maps (RMs) characterize the spatial distribution of wireless channel features and provide essential foundations for environment-aware wireless communication. Existing RM reconstruction methods mainly focus on pathloss prediction on fixed two-dimensional planes, while three-dimensional (3D) joint inference of pathloss, time of arrival (TOA), and direction of arrival (DOA) from sparse pathloss-only observations remains underexplored. In this letter, we propose HAMM-UNet, an end-to-end framework for joint reconstruction of 3D multi-modal RMs conditioned on sparse pathloss observations, building information, and base station locations. Through height-adaptive modulation and modality-specific decoding, the framework effectively captures propagation variations across different height layers and mitigates multi-modal reconstruction conflicts. Experimental results demonstrate that at a 10% sampling rate, the proposed method achieves optimal performance across all channel parameters, with a 69.4% reduction in pathloss root mean square error (RMSE) compared to the best baseline, and maintains robust performance even at sampling rates as low as 1%.