13 citations · 33 across the 18 of their papers we have counts for
20 papers
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…
RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing
Hao Zhang, Dongfang Xu, Hang Zou +7
Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a clo…
TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks
Bohao Wang, Chenwei Wu, Hang Zou +7
Large language models (LLMs) offer great potential to automate a broad range of telecom engineering tasks by reasoning over standards, network configurations, mathematical models,…
A JEPA-Based Field-Layer World Model for Bridging Channel Prediction and Estimation
Yuzhi Yang, Brahim Mefgouda, Hang Zou +5
Channel state information (CSI) acquisition, reconstruction, and prediction are fundamental yet costly tasks in modern MIMO-OFDM wireless systems. Direct coefficient-level predicti…
TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack
Bohao Wang, Chenwei Wu, Haoyu Li +8
Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specification…
LLM-Based Digital Twin Intelligence for Application-Aware Network Selection in 6G Heterogeneous Wireless Networks
Brahim Mefgouda, Anis Bara, Lina Bariah +3
Future 6G heterogeneous wireless networks (HWNs) are expected to support multiple radio access technologies (RATs), dynamic wireless environments, and applications with diverse qua…
SEM-RAG: Structure-Preserving Multimodal Graph Compilation and Entropy-Guided Retrieval for Telecommunication Standards
Yuzhi Yang, Lina Bariah, Yuhuan Lu +2
Telecommunication standards pose a unique challenge for retrieval systems, where accuracy depends on semantic relevance as well as on preserving the structural logic embedded in th…
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…
Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Hang Zou, Yuzhi Yang, Lina Bariah +15
The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language-based systems, such as Large Language Models (LLMs), and phys…
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…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models
Hang Zou, Qiyang Zhao, Yu Tian +4
Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized k…
Generative AI for RF Sensing in IoT systems
Li Wang, Chao Zhang, Qiyang Zhao +5
The development of wireless sensing technologies, using signals such as Wi-Fi, infrared, and RF to gather environmental data, has significantly advanced within Internet of Things (…
Large Language Models for Power Scheduling: A User-Centric Approach
Thomas Mongaillard, Samson Lasaulce, Othman Hicheur +6
While traditional optimization and scheduling schemes are designed to meet fixed, predefined system requirements, future systems are moving toward user-driven approaches and person…
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
Hang Zou, Qiyang Zhao, Samson Lasaulce +3
Generative Artificial Intelligence (GenAI) and communication networks are expected to have groundbreaking synergies for 6G. Connecting GenAI agents via a wireless network can poten…
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
Qiyang Zhao, Hang Zou, Mehdi Bennis +3
In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate hig…
Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence
Hang Zou, Qiyang Zhao, Lina Bariah +2
The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireles…
Understanding Telecom Language Through Large Language Models
Lina Bariah, Hang Zou, Qiyang Zhao +3
The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and depl…
Large Generative AI Models for Telecom: The Next Big Thing?
Lina Bariah, Qiyang Zhao, Hang Zou +3
The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular,…