RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse

2026-01-06

IEEE Transactions on Cognitive Communications and Networking

AI Summary

This work addresses the challenge of real-time radio map (RM) generation in 6G high-mobility scenarios, where conventional diffusion models suffer from excessive inference latency due to iterative denoising. To overcome this limitation, the authors propose a two-stage latent diffusion framework that decouples static environment modeling from dynamic refinement. A key insight is the discovery that diffusion trajectories for semantically similar scenes exhibit high consistency at their midpoints, enabling a novel midpoint reuse mechanism that eliminates redundant denoising computations. By integrating static feature encoding, latent midpoint caching, and dynamic conditional fine-tuning, the method achieves up to 50× inference acceleration with less than 0.15% degradation in accuracy, significantly enhancing both the efficiency and scalability of RM generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed network entities and fast-changing environments, it is very challenging to meet real-time requirements. Although generative diffusion models (DMs) can achieve state-of-the-art accuracy with second-level delay, their iterative nature leads to prohibitive inference latency in delay-sensitive scenarios. In this paper, by uncovering a key structural property of diffusion processes: the latent midpoints remain highly consistent across semantically similar scenes, we propose RadioDiff-Flux, a novel two-stage latent diffusion framework that decouples static environmental modeling from dynamic refinement, enabling the reuse of precomputed midpoints to bypass redundant denoising. In particular, the first stage generates a coarse latent representation using only static scene features, which can be cached and shared across similar scenarios. The second stage adapts this representation to dynamic conditions and transmitter locations using a pre-trained model, thereby avoiding repeated early-stage computation. The proposed RadioDiff-Flux significantly reduces inference time while preserving fidelity. Experiment results show that RadioDiff-Flux can achieve up to $50\times $ acceleration with less than 0.15% accuracy loss, demonstrating its practical utility for fast, scalable RM generation in future 6G networks.

Problem

• radio map
• 6G networks
• real-time construction
• diffusion models
• inference latency

Innovation

• diffusion model
• radio map construction
• latent midpoint reuse
• two-stage framework
• 6G networks