activity
20192025
most citedSecure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks

27 citations · 67 across the 19 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.DC2024

Distributed Massive MIMO-Aided Task Offloading in Satellite-Terrestrial Integrated Multi-Tier VEC Networks

Yixin Liu, Shaoling Liang, Kunlun Wang +3

This paper proposes a distributed massive multiple input multiple-output (DM-MIMO) aided multi-tier vehicular edge computing (VEC) system. In particular, each vehicle terminal (VT)…

eess.SY2024

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Kui Wang, Kazuma Nonomura, Zongdian Li +6

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside…

cs.IT2024

Static Deep Q-learning for Green Downlink C-RAN

Yuchao Chang, Hongli Wang, Wen Chen +2

Power saving is a main pillar in the operation of wireless communication systems. In this paper, we investigate cloud radio access network (C-RAN) capability to reduce power consum…

eess.SY2024

Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach

Kui Wang, Changyang She, Zongdian Li +3

Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadsid…

cs.IT2024

The Guesswork of Ordered Statistics Decoding: Guesswork Complexity and Decoder Design

Chentao Yue, Changyang She, Branka Vucetic +1

This paper investigates guesswork over ordered statistics and formulates the achievable guesswork complexity of ordered statistics decoding (OSD) in binary additive white Gaussian…

cs.NI2024★ 1 cited

Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation

Xin Hao, Changyang She, Phee Lep Yeoh +3

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios,…