Completing Radio Map Tensors via Factor Diffusion Posterior Sampling

Abstract

Radio map estimation (RME), also known as spectrum cartography, aims to recover radio signal strength across multiple domains (e.g., spatial and spectral domains) from sparse measurements, naturally leading to a tensor completion problem. Recent advances show that deep learning-based generative models can provide data-driven structural constraints for RME, yielding substantial performance improvements over traditional model-based approaches. Diffusion models (DMs) represent the current state of the art in generative modeling and hold strong promise for advancing RME. However, existing diffusion-based RME methods often require extensive training data, necessitate retraining when scenarios or measurement formats change, and lack recoverability guarantees. This paper proposes a latent factor diffusion framework for spatio-spectral radio map tensor completion. By training two independent DMs on single-emitter's spatial loss field and spectral signature and embedding them into the factors of the radio map tensor, the proposed approach enables diffusion posterior sampling-based recovery with reduced training complexity as well as enhanced measurement format/sensing pattern flexibility. Recoverability and sample complexity guarantees under the proposed approach are also established. Numerical experiments validate the effectiveness of the proposed method, showing strong generalization across city environments unseen in training data.