358 citations · 362 across the 7 of their papers we have counts for
9 papers
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control
Binghan Wu, Kai Zeng, Kai Yang +3
Operational mobile networks are rich in observations but poor in interventions: they continuously generate large volumes of passive telemetry, while controlled action--response tra…
From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)
Binghan Wu, Shoufeng Wang, Yunxin Liu +3
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive a…
Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies
Binghan Wu, Shoufeng Wang, Yunxin Liu +3
The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achie…
UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization
Kai Yang, Zelin Zhu, Chengtao Jian +4
Urban general intelligence (UGI) refers to the capacity of AI systems to autonomously perceive, reason, and act within dynamic and complex urban environments. In this paper, we int…
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Yang Liu, Kejia Zhang, Bingjie Yan +11
Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in c…
AIGC Empowering Telecom Sector White Paper_chinese
Ye Ouyang, Yaqin Zhang, Xiaozhou Ye +5
In the global craze of GPT, people have deeply realized that AI, as a transformative technology and key force in economic and social development, will bring great leaps and breakth…
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou +6
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models w…
4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP
Jiacheng Yin, Wenwen Li, Xidong Wang +2
With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated. It is…
Towards Efficient Communications in Federated Learning: A Contemporary Survey
Zihao Zhao, Yuzhu Mao, Yang Liu +4
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privac…