activity
20062025
most citedDeep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

13 citations · 88 across the 51 of their papers we have counts for

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Showing 2023Show all

13 papers · 1 filter

cs.IT2023

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach

Jiwei Zhao, Jiacheng Chen, Zeyu Sun +4

As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilizat…

cs.NI2023

Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication

Shisheng Hu, Jie Gao, Xinyu Huang +5

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system with high accuracy an…

eess.SY2023

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

Tong Liu, Lei Lei, Kan Zheng +2

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of d…

eess.SY2023

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation

Lei Lei, Tong Liu, Kan Zheng +2

In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the mul…

cs.NI2023★ 1 cited

A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

Jiacheng Wang, Hongyang Du, Dusit Niyato +6

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content gen…

cs.AI2023★ 5 cited

Artificial Intelligence for Web 3.0: A Comprehensive Survey

Meng Shen, Zhehui Tan, Dusit Niyato +7

Web 3.0 is the new generation of the Internet that is reconstructed with distributed technology, which focuses on data ownership and value expression. Also, it operates under the p…

cs.NI2023★ 4 cited

Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

Guangyuan Liu, Hongyang Du, Dusit Niyato +5

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation base…

cs.NI2023★ 6 cited

A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC

Jiayuan Chen, Changyan Yi, Hongyang Du +5

Mobile Artificial Intelligence-Generated Content (AIGC) technology refers to the adoption of AI algorithms deployed at mobile edge networks to automate the information creation pro…

cs.HC2023★ 6 cited

Realizing Immersive Communications in Human Digital Twin by Edge Computing Empowered Tactile Internet: Visions and Case Study

Hao Xiang, Changyan Yi, Kun Wu +5

Human digital twin (HDT) is expected to revolutionize the future human lifestyle and prompts the development of advanced human-centric applications (e.g., Metaverse) by bridging ph…

cs.AI2023

Guiding AI-Generated Digital Content with Wireless Perception

Jiacheng Wang, Hongyang Du, Dusit Niyato +5

Recent advances in artificial intelligence (AI), coupled with a surge in training data, have led to the widespread use of AI for digital content generation, with ChatGPT serving as…

eess.SY2023★ 1 cited

Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching

Wen Wu, Ning Zhang, Nan Cheng +4

Edge caching is emerging as the most promising solution to reduce the content retrieval delay and relieve the huge burden on the backhaul links in the ultra-dense networks by proac…

eess.SY2023

Performance Analysis and Enhancement of Beamforming Training in 802.11ad

Wen Wu, Nan Cheng, Ning Zhang +4

Beamforming (BF) training is crucial to establishing reliable millimeter-wave communication connections between stations (STAs) and an access point. In IEEE 802.11ad BF training pr…

cs.IT2023★ 1 cited

Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

Zhe Ma, Wen Wu, Feifei Gao +2

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are e…