Search Results for author: Dusit Niyato

Found 122 papers, 13 papers with code

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

no code implementations16 Sep 2023 Xu Zhang, Ziqi Lin, Shimin Gong, Bo Gu, Dusit Niyato

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT).

Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts

no code implementations5 Sep 2023 Hongyang Du, Guangyuan Liu, Dusit Niyato, Jiayi Zhang, Jiawen Kang, Zehui Xiong, Bo Ai, Dong In Kim

The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.

Artificial Intelligence for Web 3.0: A Comprehensive Survey

no code implementations17 Aug 2023 Meng Shen, Zhehui Tan, Dusit Niyato, Yuzhi Liu, Jiawen Kang, Zehui Xiong, Liehuang Zhu, Wei Wang, Xuemin, Shen

Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3. 0 and offer some insights into its potential future development direction.


Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

no code implementations15 Aug 2023 Guangyuan Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Boon Hee Soong

The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness

no code implementations29 Jul 2023 Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai, Tianhao Wu, Zehui Xiong, Jiangtian Nie, Dusit Niyato, Yang Zhang, Shengli Xie

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services.

Decision Making Federated Learning +1

Beyond Reality: The Pivotal Role of Generative AI in the Metaverse

no code implementations28 Jul 2023 Vinay Chamola, Gaurang Bansal, Tridib Kumar Das, Vikas Hassija, Naga Siva Sai Reddy, Jiacheng Wang, Sherali Zeadally, Amir Hussain, F. Richard Yu, Mohsen Guizani, Dusit Niyato

This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world.

Image Generation Text Generation

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

no code implementations22 Jul 2023 Jiayuan Chen, Changyan Yi, Hongyang Du, Dusit Niyato, Jiawen Kang, Jun Cai, Xuemin, Shen

To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges.

Federated Learning-Empowered AI-Generated Content in Wireless Networks

no code implementations14 Jul 2023 Xumin Huang, Peichun Li, Hongyang Du, Jiawen Kang, Dusit Niyato, Dong In Kim, Yuan Wu

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models.

Federated Learning

Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing

no code implementations26 Jun 2023 Ziqiang Ye, Yulan Gao, Yue Xiao, Minrui Xu, Han Yu, Dusit Niyato

We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks.

Decision Making Scheduling

Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction

no code implementations26 Jun 2023 Junlong Chen, Jiawen Kang, Minrui Xu, Zehui Xiong, Dusit Niyato, Chuan Chen, Abbas Jamalipour, Shengli Xie

Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem.

Trajectory Prediction

Towards Quantum Federated Learning

no code implementations16 Jun 2023 Chao Ren, Han Yu, Rudai Yan, Minrui Xu, Yuan Shen, Huihui Zhu, Dusit Niyato, Zhao Yang Dong, Leong Chuan Kwek

This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

Federated Learning

Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS

no code implementations6 Jun 2023 Caihong Wang, Du Xu, Zonghang Li, Dusit Niyato

The proposed framework leverages the distribution characteristics of network traffic to expand the number of minority categories in both data space and feature space, resulting in a substantial increase in the detection rate of minority categories while simultaneously ensuring the detection precision of majority categories.

Intrusion Detection

DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation

no code implementations19 Apr 2023 Yu Guo, Ryan Wen Liu, Jiangtian Nie, Lingjuan Lyu, Zehui Xiong, Jiawen Kang, Han Yu, Dusit Niyato

To eliminate the influences of adverse weather conditions, we propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement.

Management object-detection +1

Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach

no code implementations17 Apr 2023 Siyue Zhang, Minrui Xu, Wei Yang Bryan Lim, Dusit Niyato

Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption.

Multi-agent Reinforcement Learning Scheduling

Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

no code implementations30 Mar 2023 Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Abbas Jamalipour

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022.


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.

AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing

1 code implementation3 Mar 2023 Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim

Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images.

Mixed Reality

Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

no code implementations27 Feb 2023 Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang, Khoa T. Phan, Eryk Dutkiewicz, Dusit Niyato, Tao Shu

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before.

reinforcement-learning Reinforcement Learning (RL)

xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective

no code implementations23 Feb 2023 Le Xia, Yao Sun, Dusit Niyato, Daquan Feng, Lei Feng, Muhammad Ali Imran

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources.

Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework

no code implementations22 Feb 2023 Ismail Lotfi, Dusit Niyato, Sumei Sun, Dong In Kim, Xuemin Shen

Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

Marketing Multi-agent Reinforcement Learning

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

no code implementations16 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

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

no code implementations1 Feb 2023 Yong Xiao, Rong Xia, Yingyu Li, Guangming Shi, Diep N. Nguyen, Dinh Thai Hoang, Dusit Niyato, Marwan Krunz

FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset.

Federated Learning Self-Supervised Learning

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

Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

no code implementations9 Jan 2023 Hongyang Du, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Xuemin, Shen, Dong In Kim

To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.

Joint User Association and Bandwidth Allocation in Semantic Communication Networks

no code implementations29 Dec 2022 Le Xia, Yao Sun, Dusit Niyato, Xiaoqian Li, Muhammad Ali Imran

Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets).


When Quantum Information Technologies Meet Blockchain in Web 3.0

no code implementations29 Nov 2022 Minrui Xu, Xiaoxu Ren, Dusit Niyato, Jiawen Kang, Chao Qiu, Zehui Xiong, Xiaofei Wang, Victor C. M. Leung

Therefore, in this paper, we introduce a quantum blockchain-driven Web 3. 0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.

Cloud Computing

Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications

no code implementations27 Nov 2022 Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Boon Hee Soong

In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications.

Mixed Reality

Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach

no code implementations23 Nov 2022 Jiacheng Wang, Hongyang Du, Zengshan Tian, Dusit Niyato, Jiawen Kang, Xuemin, Shen

Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse.

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.

Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding

no code implementations23 Nov 2022 Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Junshan Zhang, Xuemin, Shen

To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced.

Self-Supervised Learning

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

1 code implementation7 Nov 2022 Shenglai Zeng, Zonghang Li, Hongfang Yu, Zhihao Zhang, Long Luo, Bo Li, Dusit Niyato

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse.

Federated Learning Privacy Preserving

Multi-Resource Allocation for On-Device Distributed Federated Learning Systems

no code implementations1 Nov 2022 Yulan Gao, Ziqiang Ye, Han Yu, Zehui Xiong, Yue Xiao, Dusit Niyato

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system.

Federated Learning

Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles

no code implementations1 Nov 2022 Yulan Gao, Yue Xiao, Xianfu Lei, Qiaonan Zhu, Dusit Niyato, Kai-Kit Wong, Pingzhi Fan, Rose Qingyang Hu

Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs.

Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning

1 code implementation28 Oct 2022 Yong Xiao, Zijian Sun, Guangming Shi, Dusit Niyato

A federated GCN-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized knowledge datasets.

Imitation Learning

Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks

1 code implementation IEEE Transactions on Vehicular Technology 2022 Joash Lee, Yanyu Cheng, Dusit Niyato, Yong Liang Guan, David González G.

In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols.

Autonomous Driving Distributional Reinforcement Learning +3

Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis

no code implementations25 Sep 2022 Jiawen Kang, Hongyang Du, Zonghang Li, Zehui Xiong, Shiyao Ma, Dusit Niyato, Yuan Li

Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e. g., smart healthcare.

Image Retrieval Retrieval

Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

2 code implementations10 Aug 2022 Hongyang Du, Jiazhen Liu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Junshan Zhang, Dong In Kim

Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse.

Economics of Semantic Communication System: An Auction Approach

no code implementations2 Aug 2022 Zi Qin Liew, Hongyang Du, Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato, Chunyan Miao, Dong In Kim

The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems.

Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

1 code implementation31 Jul 2022 Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Xuemin, Shen, Dong In Kim

With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i. e., attention prediction and QoE maximization.

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

no code implementations23 Jun 2022 Yunwei Tao, Yanxiang Jiang, Fu-Chun Zheng, Pengcheng Zhu, Dusit Niyato, Xiaohu You

To utilize the computing resources of other fog access points (F-APs) and to reduce the communications overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning.

Federated Learning

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

no code implementations28 May 2022 Kan Xie, Zhe Zhang, Bo Li, Jiawen Kang, Dusit Niyato, Shengli Xie, Yi Wu

However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.

Federated Learning Privacy Preserving +1

Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges

no code implementations5 Mar 2022 Hongyang Du, Jiawen Kang, Dusit Niyato, Jiayi Zhang, Dong In Kim

Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously.

Autonomous Vehicles

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

1 code implementation3 Feb 2022 Zonghang Li, Yihong He, Hongfang Yu, Jiawen Kang, Xiaoping Li, Zenglin Xu, Dusit Niyato

In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i. i. d.

Federated Learning

Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training

no code implementations31 Jan 2022 Shenglai Zeng, Zonghang Li, Hongfang Yu, Yihong He, Zenglin Xu, Dusit Niyato, Han Yu

In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.

Federated Learning Privacy Preserving

Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

no code implementations3 Jan 2022 Zhe Zhang, Shiyao Ma, Zhaohui Yang, Zehui Xiong, Jiawen Kang, Yi Wu, Kejia Zhang, Dusit Niyato

This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.

Federated Learning Privacy Preserving

Reconfigurable Holographic Surfaces for Future Wireless Communications

no code implementations13 Dec 2021 Ruoqi Deng, Boya Di, Hongliang Zhang, Dusit Niyato, Zhu Han, H. Vincent Poor, Lingyang Song

Future wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates through cost-efficient devices.

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

no code implementations20 Nov 2021 Xuezhen Tu, Kun Zhu, Nguyen Cong Luong, Dusit Niyato, Yang Zhang, Juan Li

In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training process.

Federated Learning

Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design

no code implementations26 Oct 2021 Zhe Zhang, Shiyao Ma, Jiangtian Nie, Yi Wu, Qiang Yan, Xiaoke Xu, Dusit Niyato

In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.

Federated Learning

Economics of Semantic Communication System in Wireless Powered Internet of Things

no code implementations4 Oct 2021 Zi Qin Liew, Yanyu Cheng, Wei Yang Bryan Lim, Dusit Niyato, Chunyan Miao, Sumei Sun

The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data.

Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks

no code implementations3 Sep 2021 Bo Yang, Xuelin Cao, Chongwen Huang, Yong Liang Guan, Chau Yuen, Marco Di Renzo, Dusit Niyato, Merouane Debbah, Lajos Hanzo

In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.

Autonomous Driving

6G Internet of Things: A Comprehensive Survey

no code implementations11 Aug 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.

Autonomous Driving

Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

no code implementations2 Aug 2021 Md. Shirajum Munir, Ki Tae Kim, Kyi Thar, Dusit Niyato, Choong Seon Hong

To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.

Autonomous Vehicles Scheduling

Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

no code implementations1 Jul 2021 Nguyen Quang Hieu, Dinh Thai Hoang, Dusit Niyato, Dong In Kim

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network.

reinforcement-learning Reinforcement Learning (RL)

Federated Learning for Industrial Internet of Things in Future Industries

no code implementations31 May 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.

Federated Learning

A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks

no code implementations19 Apr 2021 Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.

Privacy Preserving Transfer Learning

Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report

no code implementations13 Apr 2021 Chao Xu, Yiping Xie, Xijun Wang, Howard H. Yang, Dusit Niyato, Tony Q. S. Quek

cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.

reinforcement-learning Reinforcement Learning (RL)

Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches

no code implementations11 Mar 2021 Nguyen Thi Thanh Van, Nguyen Cong Luong, Feng Shaohan, Huy T. Nguyen, Kun Zhu, Thien Van Luong, Dusit Niyato

To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve.

Computer Science and Game Theory

Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions

no code implementations24 Feb 2021 Latif U. Khan, Walid Saad, Dusit Niyato, Zhu Han, Choong Seon Hong

Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.

Edge-computing Networking and Internet Architecture

Ambient Backscatter-Assisted Wireless-Powered Relaying

no code implementations2 Feb 2021 Xiao Lu, Dusit Niyato, Hai Jiang, Ekram Hossain, Ping Wang

With different mode selection protocols, we characterize the success probability and ergodic capacity of a dual-hop relaying system with the hybrid relay in the field of randomly located ambient transmitters.

Information Theory Networking and Internet Architecture Information Theory

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

no code implementations23 Dec 2020 Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci

Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.

reinforcement-learning Reinforcement Learning (RL)

A Comprehensive Survey of 6G Wireless Communications

no code implementations21 Dec 2020 Yang Zhao, Wenchao Zhai, Jun Zhao, Tinghao Zhang, Sumei Sun, Dusit Niyato, Kwok-Yan Lam

First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.

Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications

no code implementations18 Dec 2020 Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).

Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

no code implementations8 Dec 2020 Yi Liu, Ruihui Zhao, Jiawen Kang, Abdulsalam Yassine, Dusit Niyato, Jialiang Peng

Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes.


Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

no code implementations8 Dec 2020 Yi Liu, Xingliang Yuan, Ruihui Zhao, Cong Wang, Dusit Niyato, Yefeng Zheng

Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods.

Federated Learning Quantization

Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks

no code implementations29 Aug 2020 Shimin Gong, Jiaye Lin, Jinbei Zhang, Dusit Niyato, Dong In Kim, Mohsen Guizani

Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.

BIG-bench Machine Learning

Toward Smart Security Enhancement of Federated Learning Networks

no code implementations19 Aug 2020 Junjie Tan, Ying-Chang Liang, Nguyen Cong Luong, Dusit Niyato

In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads.

Federated Learning

Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework

no code implementations10 Aug 2020 Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu, Song Guo, Yang Zhang, Dusit Niyato, Cyril Leung, Chunyan Miao

This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation.

Cryptography and Security

Optimization-driven Hierarchical Learning Framework for Wireless Powered Backscatter-aided Relay Communications

no code implementations4 Aug 2020 Shimin Gong, Yuze Zou, Jing Xu, Dinh Thai Hoang, Bin Lyu, Dusit Niyato

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver.

Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey

no code implementations26 Jul 2020 Nguyen Cong Luong, Xiao Lu, Dinh Thai Hoang, Dusit Niyato, Dong In Kim

First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems.


Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles

no code implementations13 Jul 2020 Jer Shyuan Ng, Wei Yang Bryan Lim, Hong-Ning Dai, Zehui Xiong, Jianqiang Huang, Dusit Niyato, Xian-Sheng Hua, Cyril Leung, Chunyan Miao

The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs.

Networking and Internet Architecture Signal Processing

Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach

no code implementations7 Jul 2020 Kevin Shen Hoong Ong, Dusit Niyato, Chau Yuen

In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context.

reinforcement-learning Reinforcement Learning (RL)

Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning

no code implementations7 Jul 2020 Kevin Shen Hoong Ong, Yang Zhang, Dusit Niyato

In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.

reinforcement-learning Reinforcement Learning (RL)

Federated Learning for 6G Communications: Challenges, Methods, and Future Directions

no code implementations4 Jun 2020 Yi Liu, Xingliang Yuan, Zehui Xiong, Jiawen Kang, Xiaofei Wang, Dusit Niyato

As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications.

Federated Learning

Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications

no code implementations25 May 2020 Jiaye Lin, Yuze Zou, Xiaoru Dong, Shimin Gong, Dinh Thai Hoang, Dusit Niyato

Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver.

Reinforcement Learning (RL)

Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty

no code implementations14 May 2020 Yongchang Deng, Yuze Zou, Shimin Gong, Bin Lyu, Dinh Thai Hoang, Dusit Niyato

By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming.

A Secure Federated Learning Framework for 5G Networks

no code implementations12 May 2020 Yi Liu, Jialiang Peng, Jiawen Kang, Abdullah M. Iliyasu, Dusit Niyato, Ahmed A. Abd El-Latif

In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL.

Federated Learning

Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids

no code implementations5 May 2020 Joash Lee, Wenbo Wang, Dusit Niyato

Each household is a decentralized agent with partial observability, which allows scalability and privacy-preservation in a realistic setting.

Distributional Reinforcement Learning energy management +4

Local Differential Privacy based Federated Learning for Internet of Things

no code implementations19 Apr 2020 Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, Kwok-Yan Lam

To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.

BIG-bench Machine Learning Federated Learning +1

Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

no code implementations8 Apr 2020 Wei Yang Bryan Lim, Jianqiang Huang, Zehui Xiong, Jiawen Kang, Dusit Niyato, Xian-Sheng Hua, Cyril Leung, Chunyan Miao

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built.

Signal Processing Networking and Internet Architecture

Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach

no code implementations8 Apr 2020 Nguyen Quang Hieu, Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, Erik Elmroth

However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.

Federated Learning Management +2

Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game

1 code implementation30 Mar 2020 Wenbo Wang, Amir Leshem, Dusit Niyato, Zhu Han

We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network.

Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

1 code implementation19 Mar 2020 Yi Liu, James J. Q. Yu, Jiawen Kang, Dusit Niyato, Shuyu Zhang

Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90. 96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.

Clustering Federated Learning +2

A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network

no code implementations16 Mar 2020 Rong Xia, Yong Xiao, Yingyu Li, Marwan Krunz, Dusit Niyato

Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems.

Image Inpainting

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

no code implementations27 Feb 2020 Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Liang Xiao, Qingqing Wu

As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.

reinforcement-learning Reinforcement Learning (RL)

Optimal Pricing of Internet of Things: A Machine Learning Approach

no code implementations14 Feb 2020 Mohammad Abu Alsheikh, Dinh Thai Hoang, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han

For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers.

BIG-bench Machine Learning

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

1 code implementation27 Jan 2020 Inaam Ilahi, Muhammad Usama, Junaid Qadir, Muhammad Umar Janjua, Ala Al-Fuqaha, Dinh Thai Hoang, Dusit Niyato

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.

Autonomous Vehicles reinforcement-learning +1

Toward an Automated Auction Framework for Wireless Federated Learning Services Market

no code implementations13 Dec 2019 Yutao Jiao, Ping Wang, Dusit Niyato, Bin Lin, Dong In Kim

In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning.

Computer Science and Game Theory

Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting

no code implementations7 Dec 2019 Huimei Han, Jun Zhao, Zehui Xiong, Dusit Niyato, Wenchao Zhai, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu

Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.

Blockchain for Future Smart Grid: A Comprehensive Survey

1 code implementation8 Nov 2019 Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Kwok-Yan Lam, Xin Zhang, Amer M. Y. M. Ghias, Leong Hai Koh, Lei Yang

In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid.

Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Social and Information Networks Systems and Control Systems and Control

Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach

no code implementations21 Oct 2019 Huy T. Nguyen, Nguyen Cong Luong, Jun Zhao, Chau Yuen, Dusit Niyato

However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy.

Federated Learning Reinforcement Learning (RL)

Reliable Federated Learning for Mobile Networks

no code implementations14 Oct 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, Mohsen Guizani

Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.

Cryptography and Security

Re-route Package Pickup and Delivery Planning with Random Demands

no code implementations24 Jul 2019 Suttinee Sawadsitang, Dusit Niyato, Kongrath Suankaewmanee, Puay Siew Tan

Recently, a higher competition in logistics business introduces new challenges to the vehicle routing problem (VRP).

Stochastic Optimization

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

1 code implementation19 Jul 2019 Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network.

Cloud Computing Edge-computing +2

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

no code implementations26 Jun 2019 Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, Yingbo Liu

To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.

Edge-computing Federated Learning +1

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

no code implementations16 May 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, Dong In Kim

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.

Federated Learning

Distributed Learning for Channel Allocation Over a Shared Spectrum

no code implementations17 Feb 2019 S. M. Zafaruddin, Ilai Bistritz, Amir Leshem, Dusit Niyato

When the CSI is time varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converge to a good channel allocation.

Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

no code implementations10 Dec 2018 Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, Li-Chun Wang

In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics.

Networking and Internet Architecture

Joint Service Pricing and Cooperative Relay Communication for Federated Learning

no code implementations29 Nov 2018 Shaohan Feng, Dusit Niyato, Ping Wang, Dong In Kim, Ying-Chang Liang

However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e. g., central cloud or edge server, and the mobile devices for transferring the model update.

Cryptography and Security Computer Science and Game Theory

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

no code implementations18 Oct 2018 Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim

Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their states when the state and action spaces are small.

reinforcement-learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

no code implementations3 Oct 2018 Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Ying-Chang Liang, Dong In Kim

To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them.

reinforcement-learning Reinforcement Learning (RL) +1

Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning

no code implementations8 Sep 2018 Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Dusit Niyato, Ping Wang

To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.


Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach

no code implementations14 Aug 2018 Suttinee Sawadsitang, Dusit Niyato, Puay-Siew Tan, Ping Wang

A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning.

Stochastic Optimization

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

no code implementations7 May 2018 Wenbo Wang, Dinh Thai Hoang, Peizhao Hu, Zehui Xiong, Dusit Niyato, Ping Wang, Yonggang Wen, Dong In Kim

This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks.

Cryptography and Security

Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

no code implementations28 Feb 2018 Suttinee Sawadsitang, Siwei Jiang, Dusit Niyato, Ping Wang

Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers.

Decentralized Caching for Content Delivery Based on Blockchain: A Game Theoretic Perspective

no code implementations23 Jan 2018 Wenbo Wang, Dusit Niyato, Ping Wang, Amir Leshem

In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains.

Networking and Internet Architecture

Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach

no code implementations16 Dec 2017 Khoi Khac Nguyen, Dinh Thai Hoang, Dusit Niyato, Ping Wang, Diep Nguyen, Eryk Dutkiewicz

With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry.

Cloud Computing

Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach

no code implementations8 Nov 2017 Nguyen Cong Luong, Zehui Xiong, Ping Wang, Dusit Niyato

However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open.

Computer Science and Game Theory

Mobile Big Data Analytics Using Deep Learning and Apache Spark

no code implementations23 Feb 2016 Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han

The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era.

Activity Recognition

Deep Activity Recognition Models with Triaxial Accelerometers

no code implementations15 Nov 2015 Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, Hwee-Pink Tan

Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data.

Human Activity Recognition Temporal Sequences

Toward a Robust Sparse Data Representation for Wireless Sensor Networks

no code implementations2 Aug 2015 Mohammad Abu Alsheikh, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato

Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary.

Compressive Sensing

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

no code implementations18 May 2014 Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan

In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs).

BIG-bench Machine Learning

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