works on

From the 1 of 9 linked papers with an AI index.

most citedThrough-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

2 citations · 2 across the 1 of their papers we have counts for

collaborators

9 papers

eess.SY20262 cited

Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

Honglei Ma, Erwu Liu, Wei Ni +5

The paper surveys magnetic induction communication for through‑the‑earth applications, covering channel models, fast fading, relay designs, and network protocols for integration in…

cs.NI2026

Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

Min Hao, Yanbing Xu, Maoqiang Wu +8

Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanw…

cs.NI2026

Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications

Lingyi Cai, Yu Zhang, Ruichen Zhang +5

Agentic artificial intelligence (AI) presents a promising pathway toward realizing autonomous and self-improving wireless network services. However, resource-constrained, widely di…

cs.CR2026

Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework

Hongjuan Li, Hui Kang, Jiahui Li +6

The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges due to dynamic three-dimensional mobility patterns,…

eess.SP2025

Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey

Yang Lu, Shengli Zhang, Chang Liu +6

The rapid advancement of communication technologies has driven the evolution of communication networks towards both high-dimensional resource utilization and multifunctional integr…

cs.LG2025

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments

Pengcheng Sun, Erwu Liu, Wei Ni +4

Federated Learning (FL) is a distributed machine learning paradigm based on protecting data privacy of devices, which however, can still be broken by gradient leakage attack via pa…