1 citations · 2 across the 12 of their papers we have counts for
13 papers
Agentic UE-CoMIMO for 6G Terminals: From Virtual Antenna Augmentation to AI-Native Virtualization
Chao-Kai Wen, Yen-Cheng Chan, Lung-Sheng Tsai +2
End-user-centric collaborative MIMO (UE-CoMIMO) lets nearby devices form a virtual multi-antenna terminal to overcome the antenna limitations of individual user equipment. Extendin…
Ten Years of Deep Learning for Wireless Communications: From Learned Blocks to Deployable Wireless Intelligence
Hao Ye, Geoffrey Ye Li, Biing-Hwang Juang
Over the past decade, deep learning has evolved from a tool for replacing isolated wireless blocks into a broader methodology for developing wireless intelligence. This article tra…
Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Le Liang, Jiajia Guo, Jun Zhang +6
6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especial…
Beam Prediction Based on Multimodal Large Language Models
Tianhao Mao, Le Liang, Jie Yang +3
Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam predictio…
Multimodal-Wireless: A Large-Scale Dataset for Sensing and Communication
Tianhao Mao, Le Liang, Jie Yang +3
This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and cus…
A Wireless Foundation Model for Multi-Task Prediction
Yucheng Sheng, Jiacheng Wang, Xingyu Zhou +4
With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location,…
Large Language Models for Wireless Communications: From Adaptation to Autonomy
Le Liang, Hao Ye, Yucheng Sheng +4
The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning.…
AI/ML Life Cycle Management for Interoperable AI Native RAN
Chu-Hsiang Huang, Yuan-Chih Fan Chiang, Chao-Kai Wen +1
Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam management, channel state information (CSI)…
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
Jiaming Yu, Le Liang, Chongtao Guo +3
This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks. In particular, we consider the c…
Semantic Communication for Cooperative Perception using HARQ
Yucheng Sheng, Le Liang, Hao Ye +2
Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This perception is enabled through vehicl…
Semantic Satellite Communications Based on Generative Foundation Model
Peiwen Jiang, Chao-Kai Wen, Xiao Li +2
Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-cha…
Semantic Communication for Cooperative Perception based on Importance Map
Yucheng Sheng, Hao Ye, Le Liang +2
Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object de…
Meta Reinforcement Learning for Fast Spectrum Sharing in Vehicular Networks
Kai Huang, Le Liang, Shi Jin +1
In this paper, we investigate the problem of fast spectrum sharing in vehicle-to-everything communication. In order to improve the spectrum efficiency of the whole system, the spec…