76 citations · 127 across the 6 of their papers we have counts for
11 papers · 1 filter
Decentralized Federated Learning with Unreliable Communications
Hao Ye, Le Liang, Geoffrey Li
Decentralized federated learning, inherited from decentralized learning, enables the edge devices to collaborate on model training in a peer-to-peer manner without the assistance o…
Federated Learning and Wireless Communications
Zhijin Qin, Geoffrey Ye Li, Hao Ye
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contras…
Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
Le Liang, Hao Ye, Guanding Yu +1
It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communica…
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Le Liang, Hao Ye, Geoffrey Ye Li
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the freq…
Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel
Hao Ye, Le Liang, Geoffrey Ye Li +1
In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), in which DNNs are employed to perform several key functions, including en…
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such a…