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20212024
most citedDynamic Resource Configuration for Low-Power IoT Networks: A Multi-Objective Reinforcement Learning Method

24 citations · 30 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IT2024

Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC

Yang Huang, Miaomiao Dong, Yijie Mao +2

Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art sche…

eess.SP2023

Sparse Bayesian Learning-Based 3D Spectrum Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery

Jie Wang, Qiuming Zhu, Zhipeng Lin +5

The spectrum environment map (SEM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resourc…

eess.SP2022★ 4 cited

Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO

Minghui Wu, Zhen Gao, Yang Huang +3

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However,…

cs.IT2021★ 1 cited

An Edge Computing Paradigm for Massive IoT Connectivity over High-Altitude Platform Networks

Malong Ke, Zhen Gao, Yang Huang +4

With the advent of the Internet-of-Things (IoT) era, the ever-increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more ef…

cs.IT2021★ 24 cited

Dynamic Resource Configuration for Low-Power IoT Networks: A Multi-Objective Reinforcement Learning Method

Yang Huang, Caiyong Hao, Yijie Mao +1

Considering grant-free transmissions in low-power IoT networks with unknown time-frequency distribution of interference, we address the problem of Dynamic Resource Configuration (D…

cs.AI2021★ 1 cited

A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks

Qihui Wu, Tianchen Ruan, Fuhui Zhou +5

Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. However, their incapability of adapting to the dynamic wireless…