Search Results for author: Shiwen Mao

Found 21 papers, 2 papers with code

Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

no code implementations31 May 2024 Geng Sun, Wenwen Xie, Dusit Niyato, Fang Mei, Jiawen Kang, Hongyang Du, Shiwen Mao

We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms.


Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

no code implementations22 Mar 2024 Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu, Haibo Zhou, Yonghua Wang, Dusit Niyato, Shiwen Mao, Shengli Xie

As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys.

Edge-computing Management

Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges

no code implementations18 Mar 2024 Yang Yang, Mingzhe Chen, Yufei Blankenship, Jemin Lee, Zabih Ghassemlooy, Julian Cheng, Shiwen Mao

The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives.

Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls

no code implementations11 Mar 2024 Chengleyang Lei, Wei Feng, Peng Wei, Yunfei Chen, Ning Ge, Shiwen Mao

Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources.


Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study

no code implementations28 Jan 2024 Cong T. Nguyen, Yinqiu Liu, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Diep N. Nguyen, Shiwen Mao

Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability.

When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

no code implementations15 Jan 2024 Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han, Dong In Kim, Khaled B. Letaief

AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment.

Language Modelling Large Language Model

Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach

no code implementations23 Dec 2023 Yifeng Lyu, Han Hu, Rongfei Fan, Zhi Liu, Jianping An, Shiwen Mao

To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements.

Multi-agent Reinforcement Learning

AI Generated Signal for Wireless Sensing

no code implementations22 Dec 2023 Hanxiang He, Han Hu, Xintao Huan, Heng Liu, Jianping An, Shiwen Mao

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data.

Attribute Denoising +1

DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices

no code implementations10 Sep 2023 Guanyu Xu, Zhiwei Hao, Yong Luo, Han Hu, Jianping An, Shiwen Mao

Our objective is to achieve fast and energy-efficient collaborative inference while maintaining comparable accuracy compared with large ViTs.

Collaborative Inference Knowledge Distillation

Guiding AI-Generated Digital Content with Wireless Perception

no code implementations26 Mar 2023 Jiacheng Wang, Hongyang Du, Dusit Niyato, Zehui Xiong, Jiawen Kang, Shiwen Mao, Xuemin, Shen

Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.


Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

1 code implementation16 Feb 2023 Minrui Xu, Dusit Niyato, Junlong Chen, Hongliang Zhang, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han

In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems.

Autonomous Driving Mixed Reality

Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling

no code implementations31 Jan 2023 Yuxi Zhao, Xiaowen Gong, Shiwen Mao

We characterize the performance bounds on the training loss as a function of clients' data labeling effort, local computation effort, and reported local models.

Federated Learning

Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction

no code implementations29 Jan 2023 Jianhang Zhu, Rongpeng Li, Xianfu Chen, Shiwen Mao, Jianjun Wu, Zhifeng Zhao

On top of that, we customize its temporal and structural learning modules to further boost the prediction performance.

Graph Learning Graph Neural Network

Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

no code implementations18 Jan 2023 Minrui Xu, Dusit Niyato, Hongliang Zhang, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han

Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and MARs for RSUs to provision real-time and effective services.

Autonomous Vehicles

Wireless Sensing Data Collection and Processing for Metaverse Avatar Construction

no code implementations23 Nov 2022 Jiacheng Wang, Hongyang Du, Xiaolong Yang, Dusit Niyato, Jiawen Kang, Shiwen Mao

We observe that the collected sensing data, i. e., channel state information (CSI), suffers from a phase shift problem, which negatively affects the extraction of user information such as behavior and heartbeat and further deteriorates the avatar construction.

Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

no code implementations19 Sep 2022 Xianfu Chen, Zhifeng Zhao, Shiwen Mao, Celimuge Wu, Honggang Zhang, Mehdi Bennis

We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system.

Reinforcement Learning (RL)

Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning

1 code implementation24 May 2022 Zhiwei Hao, Guanyu Xu, Yong Luo, Han Hu, Jianping An, Shiwen Mao

In this paper, we study the multi-agent collaborative inference scenario, where a single edge server coordinates the inference of multiple UEs.

Collaborative Inference Feature Compression

A view synthesis-based 360° VR caching system over MEC-Enabled C-RAN

no code implementations1 Oct 2020 Jianmei Dai, Zhilong Zhang, Shiwen Mao, Danpu Liu

If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server.


Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

no code implementations24 Sep 2018 Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, Shiwen Mao

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming.

BIG-bench Machine Learning Clustering +2

Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

no code implementations16 May 2018 Xianfu Chen, Honggang Zhang, Celimuge Wu, Shiwen Mao, Yusheng Ji, Mehdi Bennis

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.

Edge-computing reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.