105 citations · 368 across the 59 of their papers we have counts for
13 papers · 1 filter
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…
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
Khaled B. Letaief, Yuanming Shi, Jianmin Lu +1
The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revo…
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design
Shaoming Huang, Pengfei Zhang, Yijie Mao +2
Federated learning (FL) is recognized as a key enabling technology to support distributed artificial intelligence (AI) services in future 6G. By supporting decentralized data train…
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…