51 citations · 56 across the 16 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks
Xingqiu He, Chaoqun You, Yuzhi Yang +4
Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enha…
Two-Layer Reinforcement Learning-Assisted Joint Beamforming and Trajectory Optimization for Multi-UAV Downlink Communications
Ruiqi Wang, Essra M. Ghoura, Omar Alhussein +4
Unmanned aerial vehicles (UAVs) are pivotal for future 6G non-terrestrial networks, yet their high mobility creates a complex coupled optimization problem for beamforming and traje…
Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers
Yuzhi Yang, Sen Yan, Weijie Zhou +4
With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this c…
Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks
Xin Tong, Zhaoyang Zhang, Yuzhi Yang +4
In this correspondence, a novel integrated sensing and communication (ISAC) framework is proposed to accomplish data communication, vehicle positioning, and environment sensing sim…
Non-Identical Diffusion Models in MIMO-OFDM Channel Generation
Yuzhi Yang, Omar Alhussein, Mérouane Debbah
We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency division multiplexing (OFDM) channel…
Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications
Yuzhi Yang, Omar Alhussein, Atefeh Arani +2
This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathema…
An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models
Yuqing Tian, Zhaoyang Zhang, Yuzhi Yang +5
Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future network…
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation
Yuzhi Yang, Zhaoyang Zhang, Zhaohui Yang
This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose th…
Over-the-Air Split Machine Learning in Wireless MIMO Networks
Yuzhi Yang, Zhaoyang Zhang, Yuqing Tian +4
In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease com…
Communication-Efficient Federated Learning with Binary Neural Networks
Yuzhi Yang, Zhaoyang Zhang, Qianqian Yang
Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a…