240 citations · 475 across the 17 of their papers we have counts for
5 papers · 1 filter
Learn to Compress CSI and Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centr…
Learn to Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is app…
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…