RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction (2504.14298v3)

Abstract: Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental structure perception, such as building outlines, and location of BS just relay on pathloss, through integrated sensing and communication (ISAC). Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction

In "RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction," the authors tackle the challenge of constructing radio maps (RMs) in wireless communication, particularly in environments where full environmental data and precise base station (BS) locations are unavailable. This paper presents a novel approach by reformulating RM construction as a Bayesian inverse problem. Traditionally, RM methods rely heavily on detailed environmental modeling, limiting their applicability in dynamic, unstructured, or privacy-sensitive settings. The authors propose the RadioDiff-Inverse framework to address these challenges, leveraging diffusion models within a Bayesian framework to perform robust RM reconstruction despite sparse and noisy data.

The core contribution of this paper lies in integrating generative diffusion models into the RM construction pipeline. This integration establishes an RM prior in scenarios where coarse environmental knowledge is accessible, bypassing the necessity for comprehensive environmental and BS information. Notably, the proposed method does not require task-specific fine-tuning because it utilizes a pre-trained diffusion model from Imagenet, thereby reducing the computational costs typically associated with large model training.

Experimental results underscore the prowess of RadioDiff-Inverse. The paper provides quantitative and qualitative evidence demonstrating state-of-the-art performance in RM reconstruction tasks, highlighting its superiority in both accuracy and resilience to noise compared to existing methods like RadioUNet, RME-GAN, and conventional interpolation techniques. Particularly, the method achieved PSNR values significantly higher than comparative methods, even under conditions of extreme data sparsity and noise. This underscores its potential for practical deployment in resource-constrained environments characteristic of 6G networks.

The methodology presented supports Integrated Sensing and Communication (ISAC) applications, extending beyond simply reconstructing RMs to also perceiving environmental structures such as building outlines and potential BS locations when prior information is not fully available. Such capabilities support various applications, including interference management in satellite and aerial networks, where non-cooperative transmitters challenge traditional methods.

The implications of this research stretch beyond practical applications in wireless communication to theoretical advancements in inverse problem-solving and the effective application of generative models in challenging data domains. Future research could explore varying scales of environmental elements and refine noise modeling to enhance real-world fidelity further. The authors’ integration of diffusion models into Bayesian inference could inspire further research in other domains where incomplete or noisy data challenge traditional modeling techniques.

In conclusion, RadioDiff-Inverse advances the field of radio map construction, proposing a robust framework adaptable to dynamic and information-scarce environments. It represents a significant stride toward using diffusion models to enhance modeling capabilities in complex, real-world scenarios. This work lays a foundation for future developments in both the applied framework of ISAC and broader applications of AI in unsupervised learning and probabilistic inference within wireless communication networks.