210 citations · 607 across the 38 of their papers we have counts for
38 papers
Nonparametric Variational Bayesian Learning for Channel Estimation with OTFS Modulation
Chong Cao, Zhuyu Liu, Zheng Dong +2
Orthogonal time frequency space (OTFS) modulation has demonstrated significant advantages in high-mobility scenarios in future 6G networks. However, existing channel estimation met…
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
Dingzhu Wen, Xiaoyang Li, Yong Zhou +3
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic commun…
Task-Oriented Communications for 6G: Vision, Principles, and Technologies
Yuanming Shi, Yong Zhou, Dingzhu Wen +3
Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is envisaged to go beyond data-centric services to provide intelligent and immersive…
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
Yandong Shi, Lixiang Lian, Yuanming Shi +6
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-sc…
UAV-Assisted Multi-Cluster Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multicluster networks, where multiple UAVs simultaneously perform different WDA…
Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
Zhibin Wang, Yapeng Zhao, Yong Zhou +3
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and giga…
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
Zhibin Wang, Yong Zhou, Yuanming Shi +1
Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wirele…
Reconfigurable Intelligent Surfaces Empowered Green Wireless Networks with User Admission Control
Jinglian He, Yijie Mao, Yong Zhou +2
Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and energy-efficient technique for 6G. By adjusting the phase shifts of passive reflecting elements, RIS is…
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Yuhan Yang, Yong Zhou, Youlong Wu +1
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It…
Over-the-Air Federated Learning via Second-Order Optimization
Peng Yang, Yuning Jiang, Ting Wang +3
Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and…
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang +3
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data.…
Learning Proximal Operator Methods for Massive Connectivity in IoT Networks
Yinan Zou, Yong Zhou, Yuanming Shi +1
Grant-free random access has the potential to support massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a…
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming
Shuang Liang, Yuanming Shi, Yong Zhou
Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-ante…
Over-the-Air Computation via Cloud Radio Access Networks
Lukuan Xing, Yong Zhou, Yuanming Shi
Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via expl…
Over-the-Air Decentralized Federated Learning
Yandong Shi, Yong Zhou, Yuanming Shi
In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus…
UAV Aided Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels. Different from…
Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation
Shaoming Huang, Yong Zhou, Ting Wang +1
Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guaran…
Over-the-Air Computation via Reconfigurable Intelligent Surface
Wenzhi Fang, Yuning Jiang, Yuanming Shi +3
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition prope…
Optimal Receive Beamforming for Over-the-Air Computation
Wenzhi Fang, Yinan Zou, Hongbin Zhu +2
In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple ante…
UAV-Assisted Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance tra…
Reconfigurable Intelligent Surface for Massive Connectivity
Shuhao Xia, Yuanming Shi, Yong Zhou +1
With the rapid development of Internet of Things (IoT), massive machine-type communication has become a promising application scenario, where a large number of devices transmit spo…
Optimizing Information Freshness for Cooperative IoT Systems with Stochastic Arrivals
Bohai Li, Qian Wang, He Chen +2
This paper considers a cooperative Internet of Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possi…
Federated Learning via Intelligent Reflecting Surface
Zhibin Wang, Jiahang Qiu, Yong Zhou +4
Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access…
Fast Convergence Algorithm for Analog Federated Learning
Shuhao Xia, Jingyang Zhu, Yuhan Yang +3
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end d…