A Multi-Scale Dictionary Learning Deep Unfolding Network for Radio Map Reconstruction

Investigation into the Spectral Completion Algorithm Leveraging Dense Connection Autoencoders

RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization

RadioVIL is proposed, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem and unlocks accurate zero-shot vehicle localization directly from sparse radio maps, paving a robust way for ISAC at the 6G edge.

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation Without Deployment-Time Fine-Tuning

RadioTrace is proposed, a novel RM estimation framework without deployment-time fine-tuning that tightly integrates sparse RSS measurements with a frozen pre-trained diffusion prior and achieves competitive performance with state-of-the-art learning-based methods under random sampling, and maintains strong reconstruction quality under restricted-area sampling.

Image Inpainting-Based Spectrum Occupancy Prediction in Cognitive Radio Networks Using Deep Learning

Reliable dynamic spectrum access in wide-area cognitive radio networks (CRNs) is challenged by sparse and erroneous spectrum-sensing measurements. This work formulates spatial-spectrum occupancy reconstruction as an image inpainting problem and proposes two cascaded deep learning models: a bidirectional long short-term memory-based image inpainting model (BiLSTM-IIM) and a binary diffusion-based image inpainting model (Diff-IIM). In both models, Stage 1 corrects sensing errors at observed locations, while Stage 2 reconstructs missing entries. The models are evaluated using simulations of a 2500m×2500m cognitive radio network with 50–200 secondary users, five primary users, and three spatial resolutions under representative wireless conditions. Both models generally outperform total variation and matrix completion baselines under sparse and noisy observations. Under moderate sensing errors, BiLSTM-IIM achieves accuracies of 92.91%, 93.66%, and 91.17% at the 10×10, 20×20, and 30×30 resolutions, respectively, while Diff-IIM achieves lower false-alarm rates with fewer parameters. Stage 1 reduces the sensing-error rate by approximately 58% for BiLSTM-IIM and 61% for Diff-IIM. These results support deep learning-based inpainting for wide-area spectrum occupancy reconstruction under the evaluated conditions.

Neural Fitting for Sparse Radio Map Construction in LEO Satellite Network

Recently, Low Earth Orbit (LEO) satellite networks (i.e., non-terrestrial network (NTN)), such as Starlink, have been successfully deployed to provide broader coverage than terrestrial networks (TN). Due to limited spectrum resources, TN and NTN may soon share the same spectrum, calling for fine-grained spectrum monitoring to facilitate cooperative frequency usage and interference mitigation. To this end, constructing a 4D radio map (RM)—encompassing three spatial dimensions and signal spectra—is crucial. However, such construction normally requires a massive sensor deployment and high-speed analog-to-digital converters for extensive spatial signal collection and wide power spectrum acquisition, posing significant practical challenges. In this work, we present DeepRM, a deep unsupervised learning framework that requires no ground-truth labels and leverages both neural compressive sensing (CS) and tensor decomposition (TD). First, we recast the CS process as a neural network training problem and devise a sparsity–performance balancing mechanism to reconstruct wideband spectra from sub-Nyquist samples, thereby obtaining the spectral information for each frequency. Second, a neural TD framework, jointly equipped with a lightweight Gaussian smoothing scheme, is employed to construct 3D RMs across the spatial domain for all frequencies, even with highly sparse sensor deployment. Extensive evaluations demonstrate that DeepRM achieves notably lower reconstruction error than corresponding state-of-the-art baselines, especially under limited sampling conditions. Beyond 4D RM construction, we further validated that the resulting 4D RM further supports advanced function for dynamic spectrum sharing, making it well suited for emerging LEO satellite networks.

Radio Map Updating from Streaming Spectrum Measurements via Memory-Based Online Gaussian Processes

A memory-based online sparse variational Gaussian process (M-OSVGP) method that efficiently updates radio maps from streaming spectrum measurements and extends M-OSVGP with a grid-assisted online inducing point selection (GOIPS) algorithm that dynamically adapts the number and locations of inducing points based on measurement density and spatial correlation.