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

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 acceleration with less than 0.15% accuracy loss, demonstrating its practical utility for fast, scalable RM generation in future 6G networks.

Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application

In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, specifically leveraging the capabilities of diffusion models (DMs). A rigorous theoretical foundation is established based on stochastic differential equations (SDEs), which elucidates the denoising properties of DMs in mitigating additive white Gaussian noise (AWGN) in latent semantic representations. Crucially, a closed-form analytical relationship between the signal-to-noise ratio (SNR) and the denoising timestep is derived, enabling the optimal selection of diffusion parameters for any given channel condition. To address the distribution mismatch between the received signal and the DM's training data, a mathematically principled scaling mechanism is introduced, ensuring robust performance across a wide range of SNRs without requiring model fine-tuning. Built upon this theoretical insight, we develop a latent diffusion model (LDM)-based semantic transceiver, wherein a variational autoencoder (VAE) is employed for efficient semantic compression, and a pretrained DM serves as a universal denoiser. Notably, the proposed architecture is fully training-free at inference time, offering high modularity and compatibility with large-scale pretrained LDMs. This design inherently supports zero-shot generalization and mitigates the challenges posed by out-of-distribution inputs. Extensive experimental evaluations demonstrate that the proposed framework significantly outperforms conventional neural-network-based semantic communication baselines, particularly under low SNR conditions and distributional shifts, thereby establishing a promising direction for GAI-driven robust semantic transmission in future 6G systems.