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.
Abstract
Radio maps, which estimate spatial radio-frequency characteristics from spectrum measurements, are essential for applications such as spectrum management and network planning. With the continuous arrival of spectrum measurements, conventional batch processing methods for radio map reconstruction become computationally prohibitive, as they require reprocessing all accumulated measurements for each radio map update. To address this, we propose a memory-based online sparse variational Gaussian process (M-OSVGP) method that efficiently updates radio maps from streaming spectrum measurements. Our method employs sparse variational inference and updates the posterior online by minimizing a hybrid objective that integrates newly received measurements and a memory subset of previous ones to mitigate catastrophic forgetting. To further improve posterior approximation as measurements accumulate over spatially diverse regions, we extend M-OSVGP with a grid-assisted online inducing point selection (GOIPS) algorithm. GOIPS dynamically adapts the number and locations of inducing points based on measurement density and spatial correlation, providing a more informative inducing set while maintaining computational efficiency. Extensive simulations demonstrate the effectiveness of our proposed methods in reconstruction accuracy, computational efficiency, and uncertainty quantification, compared to existing batch and online baselines across various scenarios.
This letter aims to reconstruct spatio-temporal-frequency radio maps from sparse sensor measurements to support proactive wireless resource allocation in low-altitude UAV scenarios. We propose a physically guided multiscale Bayesian neural network (PGM-BNN), which integrates a learnable distance-dependent attenuation prior, multiscale temporal-frequency Fourier encoding, and MAP-based Bayesian regularization. The physical guidance module provides lightweight propagation-related features from sparse reference sensors, while the multiscale Fourier encoding captures slow global trends and rapid local oscillations in the temporal-frequency domain. A MAP-based Bayesian neural network module is further used as a prior-regularized probabilistic regression component for sparse-data inference. Experimental results show that PGM-BNN accurately reconstructs global radio maps using only a few observation points. Compared with DPA, RadioUNet, and SCA, the proposed method achieves the best MSE, RMSE, and MAE values while maintaining competitive MAPE performance.
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.
Liu Yang, Qiang Li, Zhuo Cao et al.· IEEE Transactions on Wireles...· 0 citations
A channel knowledge map (CKM) provides location-specific channel priors and can reduce the overhead of real-time channel state information (CSI) acquisition for 6G environment-aware communications. In practice, CKM generation is often constrained by sparse and noisy measurements due to the high cost of wireless data collection. In this paper, we propose PDiff, a physics-informed conditional diffusion framework for CKM generation under sparse observations. Specifically, PDiff incorporates an analytical free-space propagation prior to capture the dominant distance-dependent attenuation trend, and combines it with environmental geometry, observation masks, and sparse observations as structured conditions. These conditional inputs guide the generation process with explicit propagation-aware, environmental, and measurement constraints. To improve inference efficiency, we further develop Prop-Cache, a training-free acceleration mechanism that reuses slowly varying intermediate features across denoising steps to reduce redundant computation during sampling. Experiments on RadioMapSeer demonstrate that PDiff outperforms a wide range of baseline methods for CKM generation.
Yu Chen, Jiao Chen, Jian Tang et al.· IEEE Transactions on Network...· 0 citations
The radio map characterizes the spatial distribution of spectrum resources within a region of interest and plays an important role in wireless network planning and spectrum management. In practice, observations are often sparse, making accurate radio map reconstruction challenging. Although deep learning–based methods can recover a radio map from sparse samples, they often suffer from two major limitations: a strong dependence on large amounts of training data and reconstructed results that may deviate from the actual physical distribution. To address this issue, this paper introduces dictionary factors to characterize radio propagation properties at different spatial scales and formulates radio map reconstruction as a multi-scale dictionary factor learning problem. Based on this formulation, we propose RadioMSDL-Net, a radio multi-scale dictionary learning network. The network consists of multiple layers with identical structures and progressively refines the radio map estimate in an iterative manner. In each layer, the dictionary factor update module learns propagation characteristics at different spatial scales, while the multi-level reconstruction module integrates cross-scale features to improve both the global structure and local details of the radio map. Extensive experiments demonstrate that RadioMSDL-Net consistently outperforms existing methods in reconstruction accuracy, computational efficiency, and cross-environment generalization.
Yazhou Sun, Longhui Wang, Xi Chen et al.· IEEE Transactions on Network...· 0 citations
Automated spectrum management systems such as radio dynamic zones must infer where transmitters are and what the spatial spectrum usage looks like within a frequency band from only a handful of monitoring sensors. This paper presents an iterative likelihood-ratio detection pipeline that first performs localization from sparse power measurements without training data and subsequently reconstructs the radio-map. The region is discretized into candidate cells, and the sensor powers follow a linear model whose sparse support is the set of active transmitters. The pipeline detects transmitters one at a time by scoring every cell with a single-source generalized likelihood ratio test (GLRT), uses beam search and physics-based filters to avoid committing early to a wrong cell, and reconstructs the power field from the recovered sources. Because the channel enters only through a precomputed propagation matrix, log-distance, terrain-integrated rough earth model (TIREM), and Sionna ray-tracing models are interchangeable inputs. The pipeline is evaluated on real POWDER-testbed measurements of five fixed transmitters in a semi-urban environment spanning roughly 2.6 km by 2.9 km, discretized into a 5 m grid of about 305,000 candidate cells, at two sparse sensor densities of 10 and 30 sensors that sample on the order of 0.01% of the grid. The pipeline reconstructs the power field to 13.8 dB RMSE at 30 sensors, and per-transmitter detection probability ranges from 0.95 for the best-positioned source down to near zero for spatially distant ones. Substituting an environment-aware propagation matrix recovers a distant transmitter’s detection probability to 0.67 with no added sensors. Since the detector consumes only received-power readings, it transfers directly to emerging low-cost sensing technologies such as RFID and backscatter sensor networks, a practical way to achieve denser monitoring that would improve the detection rates further.
Serhat Tadik, Gregory D. Durgin· IEEE Journal of Radio Freque...· 0 citations
Results show that the proposed method consistently outperforms classical path-loss modeling, interpolation, Kriging, and encoder–decoder baselines, especially when only a small fraction of measurement locations is available, support propagation-prior-guided model-aided learning as a practical approach for low-cost IoT radio-map construction and wireless signal management.
Ming-Kun Lu, A. Taparugssanagorn· IEEE Access· 0 citations
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