2 citations · 3 across the 5 of their papers we have counts for
5 papers
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks
Yinan Zou, Yong Zhou, Xu Chen +1
Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a cri…
Federated Learning via Unmanned Aerial Vehicle
Min Fu, Yuanming Shi, Yong Zhou
To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devic…
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
Shuang Liang, Yinan Zou, Yong Zhou
Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks.…
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning
Yinan Zou, Zixin Wang, Xu Chen +2
In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perfo…
Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee
Yandong Shi, Hayoung Choi, Yuanming Shi +1
Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-…