Electromagnetic Digital Twin-Enabled Closed-Loop Beam Management in ISAC Systems

Digital twin (DT) is envisioned as a key enabler of sixth-generation (6G) communication systems, evolving from offline descriptive replicas for monitoring and analysis to inthe-loop agents within digital twin networks (DTNs) that couple physical and digital worlds. Recent advances in integrated sensing and communication (ISAC)-driven electromagnetic (EM) scattering methods enable environment twinning by linking channel behaviors to EM properties of the scatterers, supporting interpretable DT states and EM-grounded optimization. However, existing studies primarily focus on DT construction and lack mechanisms for closed-loop control in wireless systems. Moreover, array-geometry mismatch can bias DT reconstruction and degrade control performance, while prior works assume known arrays. To address these gaps, we propose an EM-ISACbased closed-loop DTN framework with a hierarchical design integrating environment twinning, prior injection, and control decision into an end-to-end loop. Leveraging ISAC measurements, the proposed framework jointly reconstructs scatterer information and array-dependent forward operator and employs a low-complexity Bayesian message-passing algorithm to perform contrast inference and array calibration. The reconstructed DT guides codebook preselection to reduce training overhead and narrow candidate beams. Subsequently, downlink beamforming (BF) is performed based on DT-predicted channels, enabling latency-bounded closed-loop control. Simulation results demonstrate improved robustness and control performance under array mismatch.

On Spatial Degree-of-Freedom Analysis of Near-Field Multipath Channels for Ultra-massive MIMO Systems

The transition to near-field (NF) communications in ultra-massive multiple-input multiple-output (UM-MIMO) systems fundamentally alters the spatial degrees of freedom (DoF) of wireless channels. While the NF DoF of line-of-sight (LoS) transmission channels is well-characterized in the literature, the DoF in NF multipath scenarios remains underexplored. This paper investigates the spatial DoF of NF UM-MIMO channels under practical multipath conditions. A generic DoF metric is derived by modeling multipath propagation and analyzing the resulting eigenvalue distribution based on the Green' s function representation of the channel. The DoF contribution of each path is determined by the product of the effective electrical aperture and the subtended solid angle, and the total DoF is obtained through the effective union of spatially resolvable path contributions. A mapping between the eigenvalue distribution and multipath powers is further established. Numerical simulations and real-world NF channel measurements at 28-30 GHz with 720 array elements are conducted for validation in both LoS multipath and non-LoS scenarios. The results show that multipath propagation can significantly increase the spatial DoF and that the proposed metric accurately predicts the DoF of practical NF channels. The proposed framework provides a practical tool for DoF prediction and supports capacity analysis and spatial multiplexing design in future NF UM-MIMO systems.

ChannelAgent-Empowered Electromagnetic Space World Model: A Case Study on Agent-Driven Channel Generation for 6G AI-Native Air Interface

As sixth-generation (6G) wireless networks evolve toward increasingly heterogeneous scenarios, tasks, and service requirements, conventional artificial intelligence (AI) models remain limited in task-aware decision-making and autonomous adaptation. To address this issue, this paper first proposes a ChannelAgent-empowered electromagnetic space world model, in which wireless intelligence is organized into a closed-loop process consisting of multi-modal sensing, ChannelAgent as the intelligent core, and execution with feedback update. As a case study, agent-driven channel generation is instantiated through path loss prediction. Specifically, a task-oriented intelligent feature selection mechanism is designed by integrating reinforcement-learning-inspired policy adaptation with evolutionary search, enabling the agent to iteratively derive compact and task-suitable feature subsets according to the current scenario and performance feedback. Simulation results demonstrate superior performance in both single-scenario and multi-scenario tasks, highlighting the potential of the proposed model for autonomous, adaptive, task-oriented, and closed-loop wireless intelligence.

Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates

Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-governed problems are computationally expensive in parametric design exploration. This paper proposes a variational matrix-learning Fourier network (VMLFN) for efficient parametric multiphysics surrogate modeling. VMLFN constructs a log-space sine neural representation with randomly sampled spectral frequencies, frequency-dependent decay regulation, and embedded Dirichlet boundary conditions. With fixed hidden-layer parameters, the output-layer weights are determined by reformulating the governing PDEs into variational weak forms and enforcing the stationarity condition of the resulting energy functional. This converts physics-informed training into a linear matrix-solving problem, requiring only first-order derivatives and avoiding both high-order automatic differentiation and penalty-coefficient tuning. A heuristic frequency-scanning algorithm is further introduced to select a problem-adaptive maximum frequency that covers the dominant spectral range of the target problem. The proposed method is validated on heat conduction, solid mechanics, and Helmholtz wave propagation problems. Results from five benchmark cases demonstrate that VMLFN delivers accurate full-field predictions with substantial speedup over conventional physics-informed neural networks and repeated finite-element simulations.

Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface

As 6G advances, ubiquitous connectivity and higher capacity requirements of the air interface pose substantial challenges for accurate and real-time wireless channel acquisition in diverse environments. Conventional statistical channel modeling relies on offline measurement data from limited environments, struggling to support online applications facing diverse environments. To this end, the digital twin channel (DTC) has emerged as a novel paradigm that constructs a digital replica of the physical environment through high-fidelity sensing and predicts corresponding channel in real time utilizing artificial intelligence (AI) models. As the engine of DTC, existing AI models struggle to simultaneously achieve strong environmental generalization in real-world and end-to-end channel prediction for real time tasks. Therefore, this paper proposes a channel large model (ChannelLM)-driven DTC architecture comprising three modules: low-complexity and high-accuracy environment reconstruction based on dynamic object detection and multimodal alignment of image and point cloud data, physically interpretable environment feature extraction, and a ChannelLM core to mapping these features into generalized environment representations for multi-task channel prediction. Simulation results demonstrate that, in unseen test environments, compared with small-scale AI models, ChannelLM reduces prediction errors by 4.23 dB in channel state information prediction while achieving an end-to-end inference latency of 70 milliseconds in the real world.

Dual-Teacher Distillation with Subnetwork Rectification for Black-Box Domain Adaptation

Assuming that neither source data nor the source model is accessible, black box domain adaptation represents a highly practical yet extremely challenging setting, as transferable information is restricted to the predictions of the black box source model, which can only be queried using target samples. Existing approaches attempt to extract transferable knowledge through pseudo label refinement or by leveraging external vision language models (ViLs), but they often suffer from noisy supervision or insufficient utilization of the semantic priors provided by ViLs, which ultimately hinder adaptation performance. To overcome these limitations, we propose a dual teacher distillation with subnetwork rectification (DDSR) model that jointly exploits the specific knowledge embedded in black box source models and the general semantic information of a ViL. DDSR adaptively integrates their complementary predictions to generate reliable pseudo labels for the target domain and introduces a subnetwork driven regularization strategy to mitigate overfitting caused by noisy supervision. Furthermore, the refined target predictions iteratively enhance both the pseudo labels and ViL prompts, enabling more accurate and semantically consistent adaptation. Finally, the target model is further optimized through self training with classwise prototypes. Extensive experiments on multiple benchmark datasets validate the effectiveness of our approach, demonstrating consistent improvements over state of the art methods, including those using source data or models.

From Specialist to Large Models: A Paradigm Evolution Towards Semantic-Aware MIMO

The sixth generation (6G) network is expected to deploy larger multiple-input multiple-output (MIMO) arrays to support massive connectivity, which will increase overhead and latency at the physical layer. Meanwhile, emerging 6G demands such as immersive communications and environmental sensing pose challenges to traditional signal processing. To address these issues, we propose the ``semantic-aware MIMO'' paradigm, which leverages specialist models and large models to perceive, utilize, and fuse the inherent semantics of channels and sources for improved performance. Moreover, for representative MIMO physical-layer tasks, e.g., random access activity detection, channel feedback, and precoding, we design specialist models that exploit channel and source semantics for better performance. Additionally, in view of the more diversified functions of 6G MIMO, we further explore large models as a scalable solution for multi-task semantic-aware MIMO and review recent advances along with their advantages and limitations. Finally, we discuss the challenges, insights, and prospects of the evolution of specialist models and large models empowered semantic-aware MIMO paradigms.

A Tutorial on 3GPP Rel-19 Channel Modeling for 6G FR3 (7-24 GHz): From Standard Specification to Simulation Implementation

The upper-mid band (7-24 GHz), designated as Frequency Range 3 (FR3), has emerged as a definitive ``golden band" for 6G networks, strategically balancing the wide coverage of sub-6 GHz with the high capacity of mmWave. To compensate for the severe path loss inherent to this band, the deployment of Extremely Large Aperture Arrays (ELAA) is indispensable. However, the legacy 3GPP TR 38.901 channel model faces critical validity challenges when applied to 6G FR3, stemming from both the distinct propagation characteristics of this frequency band and the fundamental physical paradigm shift induced by ELAA. In response, 3GPP Release 19 (Rel-19) has validated the model through extensive new measurements and introduced significant enhancements. This tutorial provides a comprehensive guide to the Rel-19 channel model for 6G FR3, bridging the gap between standardization specifications and practical simulation implementation. First, we provide a high-level overview of the fundamental principles of the 3GPP channel modeling framework. Second, we detail the specific enhancements and modifications introduced in Rel-19, including the rationale behind the new Suburban Macro (SMa) scenario, the mathematical modeling of ELAA-driven features such as near-field and spatial non-stationarity, and the recalibration of large-scale parameters. Overall, this tutorial serves as an essential guide for researchers and engineers to master the latest 3GPP channel modeling methodology, laying a solid foundation for the accurate design and performance evaluation of future 6G FR3 networks.

Near-field Physical Layer Security: Robust Beamforming under Location Uncertainty

In this paper, we study robust beamforming design for near-field physical-layer-security (PLS) systems, where a base station (BS) equipped with an extremely large-scale array (XL-array) serves multiple near-field legitimate users (Bobs) in the presence of multiple near-field eavesdroppers (Eves). Unlike existing works that mostly assume perfect channel state information (CSI) or location information of Eves, we consider a more practical and challenging scenario, where the locations of Bobs are perfectly known, while only imperfect location information of Eves is available at the BS. We first formulate a robust optimization problem to maximize the sum-rate of Bobs while guaranteeing a worst-case limit on the eavesdropping rate under location uncertainty. By transforming Cartesian position errors into the polar domain, we reveal an important near-field angular-error amplification effect: for the same location error, the closer the Eve, the larger the angle error, severely degrading the performance of conventional robust beamforming methods based on imperfect channel state information. To address this issue, we first establish the conditions for which the first-order Taylor approximation of the near-field channel steering vector under location uncertainty is largely accurate. Then, we propose a two-stage robust beamforming method, which first partitions the uncertainty region into multiple fan-shaped sub-regions, followed by the second stage to formulate and solve a refined linear-matrix-inequality (LMI)-based robust beamforming optimization problem. In addition, the proposed method is further extended to scenarios with multiple Bobs and multiple Eves. Finally, numerical results validate that the proposed method achieves a superior trade-off between rate performance and secrecy robustness, hence significantly outperforming existing benchmarks under Eve location uncertainty.

Investigating Self-regulated Learning Sequences within a Generative AI-based Intelligent Tutoring System

There has been a growing trend in employing generative artificial intelligence (GenAI) techniques to support learning. Moreover, scholars have reached a consensus on the critical role of self-regulated learning (SRL) in ensuring learning effectiveness within GenAI-assisted learning environments, making it essential to capture students' dynamic SRL patterns. In this study, we extracted students' interaction patterns with GenAI from trace data as they completed a problem-solving task within a GenAI-assisted intelligent tutoring system. Students' purpose of using GenAI was also analyzed from the perspective of information processing, i.e., information acquisition and information transformation. Using sequential and clustering analysis, this study classified participants into two groups based on their SRL sequences. These two groups differed in the frequency and temporal characteristics of GenAI use. In addition, most students used GenAI for information acquisition rather than information transformation, while the correlation between the purpose of using GenAI and learning performance was not statistically significant. Our findings inform both pedagogical design and the development of GenAI-assisted learning environments.