Radio maps (RMs) characterize the spatial distribution of wireless channel features and provide essential foundations for environment-aware wireless communication. Existing RM reconstruction methods mainly focus on pathloss prediction on fixed two-dimensional planes, while three-dimensional (3D) joint inference of pathloss, time of arrival (TOA), and direction of arrival (DOA) from sparse pathloss-only observations remains underexplored. In this letter, we propose HAMM-UNet, an end-to-end framework for joint reconstruction of 3D multi-modal RMs conditioned on sparse pathloss observations, building information, and base station locations. Through height-adaptive modulation and modality-specific decoding, the framework effectively captures propagation variations across different height layers and mitigates multi-modal reconstruction conflicts. Experimental results demonstrate that at a 10% sampling rate, the proposed method achieves optimal performance across all channel parameters, with a 69.4% reduction in pathloss root mean square error (RMSE) compared to the best baseline, and maintains robust performance even at sampling rates as low as 1%.
Integrated sensing and communication (ISAC) enables wireless systems to reuse communication signals for environmental sensing, where reconstructing the geometry of surrounding objects is a representative sensing task. However, many conventional methods rely on coherent processing and require accurate phase information, which is often hard to guarantee in practical communication systems, particularly at high carrier frequencies. To address this problem, this paper proposes a Multi-View Likelihood Accumulation Geometry Reconstruction (MVLA-GR) method based on channel impulse response (CIR) measurements, which uses only delay and power observations without requiring phase information. The method extracts dominant multipath components from each observation, and for each candidate spatial location, accumulates components across views whose propagation distances match the location as supporting evidence. A soft distance-matching kernel is introduced to tolerate range estimation errors and viewpoint-dependent scattering migration, and the received power of each component is used as a reliability weight. A joint thresholding strategy combining response magnitude and angular support continuity then converts the continuous support map into a binary geometry estimate. Ray-tracing simulations on canonical and complex targets, as well as real-world vehicle measurements at 36 GHz, demonstrate that MVLA-GR can effectively recover target geometry, providing a low-complexity phase-free solution for ISAC.
Bowei Xing, Yu-Xiang Zhang, Jian-Hua Zhang et al.· 0 citations
Channel knowledge maps (CKMs) have emerged as a promising technique for providing scene-specific and location-dependent propagation knowledge to enable environment-aware wireless network design. This paper investigates the construction and dynamic updating of a particular type of CKM, namely grid-based channel gain maps (CGMs), for large-scale networks using three-dimensional Gaussian splatting (3DGS). First, we formulate a grid-based channel gain model, where each map entry is defined as the locally averaged channel gain over a receiver grid, thereby suppressing phase-sensitive small-scale fluctuations. The resulting channel gain is decomposed into distance-dependent attenuation, path transmittance, and effective scattering contributions. Based on this decomposition, we develop a physics-informed Gaussian-splatting-based channel gain (GS-CG) model, which represents the propagation environment as a set of Gaussian primitives. The proposed model maps Gaussian geometry, opacity, and directional features to propagation-related factors and renders grid-level channel gains through a differentiable process. To accommodate real-time environmental changes, we further propose an incremental learning mechanism that updates a static reference GS-CG representation into a dynamic CGM. Specifically, the reference Gaussian primitives are frozen, while a compact set of tunable Gaussians is introduced to capture newly induced local channel-gain variations from sparse measurements.Numerical results demonstrate that the proposed GS-CG methods accurately reconstruct grid-based CGMs, efficiently adapt to dynamic environmental changes, and achieve a favorable accuracy-complexity tradeoff for fast CGM refinement.
Wireless signal reconstruction is essential for RF-based positioning in GPS-denied environments. However, multipath propagation, shadowing, and non-Gaussian noise complicate this, and traditional methods require extensive site-specific calibration that precludes rapid deployment. We present In-Context Signal Completion (ICSC), demonstrating that small language models fine-tuned with Group Relative Policy Optimization and physics-informed rewards can reconstruct RSSI across sequential extrapolation and spatial interpolation tasks. Our 0.5B-parameter model attains 55% recall within 2 dB and a 2.85 dB mean absolute error on sequential prediction. This achieves a 49% error reduction over the untrained baseline, performing on par with GPT-4o (51%) with fewer parameters. Successful zero-shot transfer to spatial interpolation indicates the model acquires transferable physical reasoning rather than task-specific memorization. Operating at 3 ms latency for real-time edge inference, ICSC reduces deployment from weeks of per-site data collection to immediate inference using sequential context.
This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I
Zi-Jiao Hu, Haiyao Yu, Gaoyang Pang et al.· 0 citations
Toward the realization of 6G networks and wireless digital twins, site-specific channel modeling that accurately replicates real-world environments is indispensable. While conventional map-based hybrid models capture spatial structures, they struggle to accurately reproduce local shadowing and microscopic fading characteristics due to inherent ray-tracing computational errors and the limitations of purely statistical models. In this paper, we propose a novel framework for generating high-fidelity channels by integrating site-specific propagation parameters predicted via machine learning (ML). The proposed method employs a Residual Network (ResNet) to predict path loss (PL) and delay spread (DS) from spatial features and uses these values to appropriately scale and correct the cluster powers in the hybrid model. To validate its generalizability, we conducted a rigorous ablation study comparing five parameter configurations using urban measurement data at 8.45 GHz in the FR3 upper mid-band—a key candidate spectrum for 6G—across two continuous routes with distinct propagation topologies. The evaluation results demonstrate that the proposed method accurately tracks environment-specific macroscopic trajectories while eliminating the inherent overestimation bias of conventional ray-tracing. Furthermore, in evaluating microscopic small-scale fading, the proposed method improved the Kolmogorov-Smirnov (KS) distance by approximately 9–17% compared with conventional methods. Most notably, spatial autocorrelation analysis demonstrated that our method successfully mitigates the critical spatial inconsistency of conventional models, deterministically reproducing decorrelation distances of 35.4 m and 46.0 m on the two evaluation routes under the route-exclusive split, in close agreement with the measured values (36.8 m and 46.0 m), which themselves align with the 3GPP UMa standard parameters (37 m for LoS / 50 m for NLoS). These findings establish the proposed framework as a powerful next-generation methodology for highly realistic wireless digital twins.
Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes. To bridge this gap, we propose GLocFM, a Geometry-aware Localization Foundation Model, which jointly exploits WiFi measurements and scene geometry represented as a 3D point cloud. We formulate localization as a maximum-likelihood (ML) estimation problem, where the goal is to find a transmitter position that maximizes the likelihood of the wireless observations conditioned on the scene geometry. The likelihood of a candidate transmitter position is calculated by a learned scoring function that matches the observed delay--angle-of-arrival (AoA) spectrum against the spectrum predicted for that candidate. A hierarchical scene encoder extracts propagation-relevant features to produce geometric priors for LoS and one-bounce reflection paths. For scenarios with imperfect synchronization, we further introduce a time-of-flight (ToF)-robust GLocFM model to handle unknown ToF offsets. GLocFM is trained on a multi-modal synthetic indoor localization dataset comprising 221 diverse scenes whose associated wireless signals are generated using Sionna RT. On both synthetic and the NeRF$^{2}$ dataset based on real measurements, GLocFM reduces mean 3D localization error relative to one of the state-of-the-art localization baselines by 49.5\% and 48.8\%, respectively. Ablations across different number of receiver, bandwidths, and array sizes further demonstrate the effectiveness and robustness of the proposed framework.
Chenghong Bian, Chaozheng Wen, Hongze Chen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
