109 citations · 436 across the 106 of their papers we have counts for
114 papers
Learning or Modeling? Virtual XL-RIS Observation Reconstruction for Near-Field Sensing
Tuo Wu, Jie Tang, Jiacheng Wang +5
A passive extremely large-scale reconfigurable intelligent surface (XL-RIS) offers a position-sensitive aperture for near-field sensing, but its element-wise signals are not direct…
DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
Bohao Wang, Tianen Yuan, William Yen +12
Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limi…
Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
Hongning Ruan, Zhaoyang Zhang, Zirui Chen +3
The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete po…
Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach
Can Zheng, Jiguang He, Guofa Cai +2
Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual o…
Channel Gains to Captions: Task-Unified Multi-Level RF Sensing with Vision-Language Models
Tianyu Hu, Zhiren Gong, Haowei Cui +6
This letter investigates a task-unified multi-level radio-frequency (RF) sensing framework driven by vision-language models (VLMs). Existing RF sensing methods rely on task-specifi…
Electromagnetic Twin: From Sparse Measurements to Persistent Wireless Intelligence
Tuo Wu, Lifeng Mai, Jie Tang +7
Radio maps and channel knowledge maps provide reusable propagation knowledge, but a stored map can become locally outdated after persistent changes to doors, partitions, furniture,…
Electromagnetic Neural Network for Direction-of-Arrival Estimation
Shining Lin, Jiancheng An, Lu Gan +4
Accurate and real-time direction of arrival (DOA) estimation is crucial for beamforming in unmanned aerial vehicle (UAV) communication systems. However, the existing high-precision…
Active Inference as the Test-Time Scaling Law for Physical AI Agents
Omar Hashash, Christo Kurisummoottil Thomas, Walid Saad +3
In this paper, a novel test-time scaling law for physical artificial intelligence (AI) agents is introduced. This scaling law enables physical AI agents to reason with their world…
Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction
Zirui Chen, Ziqing Xing, Zhaoyang Zhang +5
Data representation is a fundamental issue in deep learning. However, as wireless data scales and deeply couples across many physical domains such as time, space, and frequency, ex…
ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency
Yuwei Wang, Li Sun, Tingting Yang +4
Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communica…
Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks
Hatim Chergui, Claudia Carballo González, Farhad Rezazadeh +1
This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents.…
RF-Analyzer: Can Vision-Language Models Learn RF Understanding from Synthetic Data?
Anis Bara, Lina Bariah, Hang Zou +2
Understanding the wireless spectrum is a fundamen- tal requirement for intelligent communication systems, however, interpreting spectrograms requires extracting multiple physical a…
Differential Spatial Modulation with Transmit Diversity for Pinching-Antenna Systems
Yiwei Tao, Yao Ge, Dong Li +2
Pinching antenna (PA) systems provide a new spatial degree of freedom by flexible activation of pinching positions. However, the resulting effective channel strongly depends on the…
Distributed Electromagnetic Neural Networks for Task-Oriented Semantic Communications
Jinbao Li, Jiancheng An, Hao Liu +4
Semantic communications (SemCom) is a promising paradigm that prioritizes the transmission of task-relevant information, thereby enabling superior communication efficiency over tra…
Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations
Weijie Zhou, Zhaoyang Zhang, Yuzhi Yang +3
Acquiring the channel state information from limited and noisy observations at pilot positions is critical for wireless multiple-input multiple-output (MIMO)-orthogonal frequency d…
JEPA-MSAC: A Joint-Embedding Predictive Architecture for Multimodal Sensing-Assisted Communications
Can Zheng, Jiguang He, Guofa Cai +4
Future wireless systems increasingly require predictive and transferable representations that can support multiple physical-layer (PHY) tasks under dynamic environments. However, m…
Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
Esraa Fahmy Othman, Lina Bariah, Merouane Debbah
Beam alignment is a key challenge in directional mmWave and THz systems, where narrow beams require accurate yet low-overhead training. Existing learning-based approaches typically…
Two-Layer Stacked Intelligent Metasurfaces: Balancing Performance and Complexity
Hong Niu, Chau Yuen, Marco Di Renzo +2
Stacked intelligent metasurfaces (SIMs) have emerged as a powerful paradigm for wave-domain signal processing, enabling fine-grained control over electromagnetic (EM) propagation i…
Neural Precoding in Complex Projective Spaces
Zaid Abdullah, Merouane Debbah, Symeon Chatzinotas +1
Deep-learning (DL)-based precoding in multi-user multiple-input single-output (MU-MISO) systems involves training DL models to map features derived from channel coefficients to lab…
RF-GPT: Teaching AI to See the Wireless World
Hang Zou, Yu Tian, Bohao Wang +4
Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, a…
Seeing Radio: From Zero RF Priors to Explainable Modulation Recognition with Vision Language Models
Hang Zou, Bohao Wang, Yu Tian +4
Current RF machine-learning pipelines rely on task-specific deep networks for modulation classification and related tasks, but these models require custom architectures and labeled…
One-Step Generative Channel Estimation via Average Velocity Field
Zehua Jiang, Fenghao Zhu, Siming Jiang +5
Generative models have shown immense potential for wireless communication by learning complex channel data distributions. However, the iterative denoising process associated with t…
Stacked Intelligent Metasurface-Aided Wave-Domain Signal Processing: From Communications to Sensing and Computing
Jiancheng An, Chau Yuen, Marco Di Renzo +3
Artificial neural networks possess remarkable capabilities for abstract feature extraction, while electromagnetic computing leverages wave propagation to execute complex mathematic…
Recursive Flow: A Generative Framework for MIMO Channel Estimation
Zehua Jiang, Fenghao Zhu, Chongwen Huang +5
Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In…