Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks

 With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...

DEEP LEARNING Multi-Agent Deep Reinforcement Learning

Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels

 This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a...

Multi-Agent DRL for Task Offloading and Resource Allocation in Multi-UAV Enabled IoT Edge Network

 The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networ...