most citedPath Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning

9 citations · 25 across the 9 of their papers we have counts for

collaborators

14 papers

cs.IT20204 cited

Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic

Xiongwei Wu, Xiuhua Li, Jun Li +3

Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs tha…

cs.IT20202 cited

Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization

Xiongwei Wu, Jun Li, Ming Xiao +2

Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, co…

eess.SP20202 cited

Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications

Chandan Pradhan, Ang Li, Lingyang Song +3

In this paper, we focus on the reconfigurable intelligent surface (RIS)-enhanced two-way device-to-device (D2D) multi-pair orthogonal-frequency-division-multiplexing (OFDM) communi…

eess.SP2020

Subcarrier Assignment and Power Allocation for SCMA Energy Efficiency

Samira Jaber, Wen Chen, Kunlun Wang +1

In this paper we propose resource allocation algorithm for uplink sparse code multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to the joint optimization…

eess.SP2020

Probabilistic Caching for Small-Cell Networks with Terrestrial and Aerial Users

Fei Song, Jun Li, Ming Ding +5

The support for aerial users has become the focus of recent 3GPP standardizations of 5G, due to their high maneuverability and flexibility for on-demand deployment. In this paper,…

eess.SP2020

Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing

Fei Song, Jun Li, Chuan Ma +3

The fifth generation and beyond wireless communication will support vastly heterogeneous services and use demands such as massive connection, low latency and high transmission rate…