2 citations · 3 across the 32 of their papers we have counts for
10 papers · 1 filter
Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework
Yijie Bian, Wei Guo, Jie Yang +4
High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive be…
Sensing-Assisted Channel Estimation for Flexible-Antenna Systems: A Unified Framework
Ruoxiao Cao, Wentao Yu, Zixin Wang +4
Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-effic…
Rethinking Mutual Coupling in Movable Antenna MIMO Systems: Modeling and Optimization
Tianyi Liao, Wei Guo, Jun Qian +4
Movable antennas (MAs) have attracted growing interest for their ability to improve channel conditions via adaptive antenna movement. Nevertheless, such movement inevitably introdu…
Rethinking Mutual Coupling in Movable Antenna MIMO Systems
Tianyi Liao, Wei Guo, Jun Qian +3
Movable antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of antennas gives rise to mutual coupling (MC) effects,…
Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling
Li Zeng, Jingyang Zhu, Zixin Wang +2
Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliv…
Service Function Chain Routing in LEO Networks Using Shortest-Path Delay Statistical Stability
Li Zeng, Zixin Wang, Yuanming Shi +1
Low Earth orbit (LEO) satellite constellations have become a critical enabler for global coverage, utilizing numerous satellites orbiting Earth at high speeds. By decomposing compl…
Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment
Yijie Bian, Wei Guo, Jie Yang +4
Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam manag…
Decentralized Federated Learning With Energy Harvesting Devices
Kai Zhang, Xuanyu Cao, Khaled B. Letaief
Decentralized federated learning (DFL) enables edge devices to collaboratively train models through local training and fully decentralized device-to-device (D2D) model exchanges. H…
Modular Foundation Model Inference at the Edge: Network-Aware Microservice Optimization
Juan Zhu, Zixin Wang, Shenghui Song +2
Foundation models (FMs) unlock unprecedented multimodal and multitask intelligence, yet their cloud-centric deployment precludes real-time responsiveness and compromises user priva…
Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning
Hang Zhao, Hongru Li, Dongfang Xu +2
Semantic communication is emerging as a key enabler for distributed edge intelligence due to its capability to convey task-relevant meaning. However, achieving communication-effici…