Nguyen Van Huynh received his B.E. degree in Electronics and Telecommunications Engineering from Hanoi University of Science and Technology (HUST), Vietnam in 2016. From 2017 to 2018 he worked as a research assistant at Nanyang Technological University (NTU), Singapore. He is currently a PhD student at University of Technology Sydney (UTS), Australia. His research interests include wireless powered communications, green communications, and optimisation problems for wireless communication networks.
- Student Member, IEEE.
- Student Member, IEEE Communications Society
2. TPC Member
- IEEE Vehicular Technology Conference (VTC) 2019-Spring, Kuala Lumpur, Malaysia.
- IEEE Vehicular Technology Conference (VTC) 2018-Fall, Chicago, US.
- IEEE Journal on Selected Areas in Communications (JSAC)
- IEEE Transactions on Wireless Communications (TWC)
- IEEE Transactions on Communications (TCOM)
- IEEE Transactions on Cognitive Communications and Networking (TCCN)
- IEEE Transaction on Mobile Computing (TMC)
- IEEE System Journal (ISJ)
- IEEE Transactions on Vehicular Technology (TVT)
- IEEE Wireless Communications Letters
- IEEE GLOBECOM , UAE, 2018.
- International Conference on Computing, Networking and Communications (ICNC), Hawaii, US, 2018.
- Deep Reinforcement Learning, Wireless Energy Harvesting, Internet of Things.
- 5G Networks, Performance Analysis, Cybersecurity.
- Optimization problems, Markov decision process.
- Network virtualization, Network function virtualization.
- Energy-Efficient networking, Quality of Service.
- The Future Internet, SDN, NFV.
- IoT Security
Van Huynh, N, Thai Hoang, D, Nguyen, DN & Dutkiewicz, E 2019, 'Optimal and Fast Real-Time Resource Slicing with Deep Dueling Neural Networks', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 6, pp. 1455-1470.View/Download from: UTS OPUS or Publisher's site
© 1983-2012 IEEE. Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches.
Van Huynh, N, Hoang, DT, Nguyen, DN, Dutkiewicz, E, Niyato, D & Wang, P 2019, 'Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning', IEEE Transactions on Communications, pp. 1-1.View/Download from: Publisher's site
Nguyen, H, Nguyen, D, Dinh, H & Dutkiewicz, E 2019, 'Jam Me If You Can: Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS.
Van Huynh, N, Hoang, DT, Lu, X, Niyato, D, Wang, P & Kim, DI 2018, 'Ambient Backscatter Communications: A Contemporary Survey', Communications Surveys and Tutorials, IEEE Communications Society, vol. 20, no. 4, pp. 2889-2922.View/Download from: UTS OPUS or Publisher's site
IEEE Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.
Van Huynh, N, Hoang, DT, Niyato, D, Wang, P & Kim, DI 2018, 'Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks', IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 820-823.View/Download from: UTS OPUS or Publisher's site
© 2012 IEEE. This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF signals, the sensors are able to backscatter the RF signals to transmit data to the gateway and/or harvest energy from the RF signals for their operations. By integrating backscattering and energy harvesting techniques, we can optimize the network throughput of the system. In particular, we first formulate the time scheduling problem for the system, and then propose an optimal solution using convex optimization to maximize the overall network throughput. Numerical results show a significant throughput gain achieved by our proposed design over two other baseline schemes.
Nam, TM, Thanh, NH, Hieu, HT, Manh, NT, Huynh, NV & Tuan, HD 2017, 'Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization', Computer Networks, vol. 125, pp. 76-89.View/Download from: UTS OPUS or Publisher's site
© 2017 Elsevier B.V. Cloud computing has emerged in recent years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). As the number of cloud service provider increases, there exists a demand to dynamically provision virtual data centers (VDC) on top of the infrastructure provider's physical data centers. This research addresses problems related to energy and resource efficiently embedding virtual data centers inside physical data centers under dynamic resource allocation conditions, in which VDCs continuously join and leave the system. Dynamic VDC embedding is challenging as it is an NP-hard problem that should meet multiple objectives. In this article, we propose heuristic joint VDC embedding – server consolidation approaches as one solution for that problem. Evaluation results show that the joint approach outperforms existing ones in terms of resource and energy efficiency and can keep system complexity acceptable.
Huu, TN, Anh, VV, Duc, LN, Van, HN, Manh, NT, Quynh, TN, Thu-Huong, T, Tai, HN & Magedanz, T 2015, 'A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN', COMPUTER NETWORKS, vol. 92, pp. 251-269.View/Download from: Publisher's site
Nguyen, T, Nguyen, H, Dinh, H, Nguyen, D & Dutkiewicz, E 2018, 'Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks', 2018 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates.View/Download from: UTS OPUS or Publisher's site
Nguyen, H, Dinh, H, Nguyen, D, Dutkiewicz, E, Niyato, D & Wang, P 2018, 'Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter', 2018 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, UAE.View/Download from: UTS OPUS or Publisher's site
Tran, MN, Nguyen, VH & Nguyen, HT 2016, 'Reducing Middle Nodes Mapping Algorithm for Energy Efficiency in Network Virtualization', ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY, International Conference on Advances in Information and Communication Technology, SPRINGER INT PUBLISHING AG, Thai Nguyen, VIETNAM, pp. 500-509.View/Download from: Publisher's site
For the future of the Internet, Network Virtualization and Software-Defined Networking (SDN) are recognized as key technologies. They reshape computing and network architectures, provide a number of advantages including centralized management, scalability and resource optimization. Along with the Internet's development, the networking devices such as routers and switches are notable part of the large energy consumption of ITC. The design of high performance and energy-efficient network by using virtualization and SDN becomes an importance issue. In this paper we heuristic present energy-efficient algorithms for virtual network embedding. The experimental results show the remarkable energy-saving level of their approaches while maintaining the acceptance ratio.
Usman, M, Risdianto, AC, Han, J, Kim, J & Van Huynh, N 2017, 'Physical-virtual topological visualization of OF@TEIN SDN-enabled multi-site cloud', International Conference on Information Networking, International Conference on Information Networking, IEEE, Da Nang, Vietnam, pp. 622-624.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Infrastructure visualization based on monitored resource status is essential for the effective operation of modern SDN (Software-Defined Networking)-enabled cloud. More specifically, collecting and visualizing visibility information about both physical & virtual (p+v) types of resources is one of key requirement for multi-site cloud operators. However, most existing visualization tools fall short in effectively providing the p+v topological visualization of multi-site cloud infrastructure. Thus, in this paper, by taking an example of OF@TEIN (OpenFlow @ Trans-Eurasian Information Network) multi-site experimental cloud, we attempt to realize visualization software to assist both developers and operators in capturing the p+v topological visualization of infrastructure resources.