Journal Information
IEEE Network
http://www.comsoc.org/netmag
Impact Factor:
8.808
Publisher:
IEEE
ISSN:
0890-8044
Viewed:
18656
Tracked:
31
Call For Papers
Welcome to IEEE Network - The Magazine of Global Internetworking. IEEE Network, published bimonthly, offers readers topics of interest to the networking community. As such, IEEE Network provides a focus for highlighting and discussing major computer communications issues and developments. The articles are intended to be surveys or tutorials, slanted towards the practical, and comprehensible to the nonspecialist, as well as practitioners.
Last updated by Dou Sun in 2022-04-04
Special Issues
Special Issue on Native Artificial Intelligence in Integrated Terrestrial and Non-Terrestrial Networks in 6G
Submission Date: 2022-05-21

The upsurge of interest in the sixth generation (6G) wireless networks, driven by the emergence of novel data-hungry applications, such as virtual/augmented/mixed reality (VR/AR/MR) services, tactile internet, haptic applications, autonomous systems, and holographic-type communications, is pushing the current infrastructure to its limits. This necessitates a radical departure from the conventional ground communications to innovative paradigms, such as integrated terrestrial and non-terrestrial networks (TNTNs), which incorporates terrestrial, air and space layers to enable multi-layer communications and extend the current network capabilities and resources. Albeit the potential advantages, in terms of throughput, coverage, and resilience, the deployment of integrated TNTNs poses new challenges, pertaining to the enormous amount of data and network traffic produced, exchanged, and managed in both inter- and intra- layer communications. It has been shown recently that the uniqueness of 6G networks, compared to previous wireless network generations, lies in the realization of ubiquitous intelligence, in which native artificial intelligence (AI) will be the key to orchestrate wireless networks from the core to the edge, and to the cloud. To this end, machine learning (ML), which is a subfield of AI, is anticipated to be an indispensable tool in future 6G networks, which operates on the data collected from all network segments in order to enable smart resource management, access control, and multi-layer communications. However, the anticipated vision for 6G networks goes beyond leveraging ML to replace particular modules in the network. Rather, it is envisioned that each network node will enjoy a level of intelligence that enables it to continuously learn from the environment, and therefore, adapt to the network changes. The inherent heterogeneous characteristics/requirements of different nodes in each layer and among different layers noticeably exacerbate the communication management and coordination difficulty, owing to the resulted heterogeneous data. Additionally, in conventional ML algorithms, raw data generated and stored at local devices should be sent to centralized servers for processing, training, and aggregation, yielding compromised users’ privacy and security, and increased network overhead. Furthermore, centralized ML (CML) suffers from long propagation delay, rendering it unsuitable for real-time applications. These challenges are particularly pronounced in integrated TNTNs. Motivated by the increasing demand for secure AI tools, and the enhanced on-board computing and storage capabilities of wireless devices, the research has started to shift from centralized to distributed learning approaches. In this respect, distributed ML (DML), including the federated learning (FL), has been recently identified as an enabling technology that is capable of training wireless networks without leaking private information or consuming network resources [4]. In particular, DML allows a set of local devices to locally and collaboratively participate in the training process of a global model without having to upload their raw local data to centralized servers. Although DML has received significant attention in the context of wireless networks, the research on the implementation of DML in single and multiple layers in integrated TNTNs is still in its infancy. In particular, several design aspects and challenges, pertaining to inter- and intra- layer communication, including but are not limited to, client selection and scheduling, joint communication and learning, model aggregation and compression, data imbalance, model convergence rate, and resource allocation, are yet to be addressed. The objective of this Special Issue (SI) is to solicit research papers with original contributions that address the latest advances and challenges in distributed and centralized ML-enabled satellite, aerial, ground, and integrated networks, paving the way for the efficient realization and integration of DML and CML in future 6G networks. More specifically, this SI will bring together leading researchers from both industry and academia to present their views on this emerging research with respect to the fundamentals, core design aspects, applications, use-cases, and challenges of CML and DML empowered wireless networks. The papers will be peer reviewed by at least three independent experts and will be selected on their relevance to the theme of this SI. Topics of interest include, but are not limited to: Architecture design and algorithms of DML & CML CML/DML-enabled ground communications CML/DML-enabled aerial networks CML/DML-enabled satellite networks CML/DML-enabled integrated TNTNs CML/DML for secure PHY-layer CML/DML-enabled TNTNs for IoT CML/DML-based trajectory optimization in Aerial networks Asynchronous CML/DML for edge devices Client selection and scheduling in DML Joint communication, sensing, and learning Model compression and aggregation in DML Efficient schemes for data imbalance in CML/DML CML/DML for channel modeling and estimation in integrated TNTNs Privacy and efficiency trade-off in CML/DML Enhanced security, privacy, and trust in TNTNs through CML/DML The interplay of CML/DML and blockchain for secure TNTNs Distributed and centralized machine learning algorithms in mobile edge computing Distributed and centralized machine learning empirical studies Distributed and centralized machine learning applications in TNTNs Energy efficient techniques for improved TNTNs through CML/DML CML/DML in emerging applications Network protocol designs for CML/DML in terrestrial, aerial, and satellite networks Optimization of CML/DML-enabled TNTNs Efficient schemes for data heterogeneity and dependency in DML-enabled networks Meta-learning Multi-task optimization and learning CML/DML for resource management in TNTNs The interplay between CML/DML and reconfigurable intelligent surfaces in TNTNs Federated learning-enabled TNTNs Incentive mechanisms design and game-theoretic approaches for ML-enabled TNTNs CML/DML and optical wireless communication in satellite, aerial, and terrestrial networks. The topics covered by the proposed IEEE Network SI are aimed to be the foundation for the revolution of new CML & DML paradigms to be implemented vertically over multiple layers, including, the space, air, and ground. Submission Guidelines Manuscripts should conform to the standard format as indicated in the "Information for Authors" section of the Paper Submission Guidelines. All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select “November 2022/AI-TNTN” from the drop-down menu of topic titles. Important Dates Manuscript Submission Deadline: 31 May 2022 Initial Decision Notification: 15 July 2022 Revised Manuscript Due: 15 August 2022 Final Decision Notification: 31 August 2022 Final Manuscript Due: 15 September 2022 Publication Date: November/December 2022 Guest Editors Lina Bariah KU C2PS, Khalifa University UAE/University at Albany, SUNY, USA Sami Muhaidat KU C2PS, Khalifa University UAE/Carleton University, Canada Daniel Benevides Da Costa Technology Innovation Institute, UAE Guosen Yue Futurewei Technologies, Inc, USA Ekram Hossain University of Manitoba, Canada Merouane Debbah Technology Innovation Institute, UAE, and Lagrange Mathematical and Computing Research Center, France
Last updated by Dou Sun in 2022-04-04
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