IEEE Communications Magazine is a hybrid open access periodical. IEEE has recently signed an agreement with academic institutions in your country offering the possibility for authors affiliated to these institutions to publish in open access periodicals at no additional cost (more details at Institutional OA Agreements - IEEE Open).

https://open.ieee.org/for-institutions/institutional-partners/

IEEE Communications Magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications systems (PCS), ISDN, and more. It includes special feature technical articles and monthly departments: book reviews, conferences, short courses, standards, governmental regulations and legislation, new products, and Society news such as administration and elections.

IEEE Communications Magazine (ComMag) is a flagship publication of the IEEE Communications Society and the world’s most recognized magazine in Telecommunications with a top-ranking Impact Factor.
ComMag serves its broad readership by publishing highest-quality, accessible and tutorial papers in three main tracks:
1) regularly scheduled Series addressing selected areas in the telecommunications field,
2) individually from open call on an ongoing basis, and
3) as part of very selective Feature Topics (FTs) which focus on emerging trends and hot subjects.

We would like to invite you to submit your manuscript to
IEEE Communications Magazine Series on
ARTIFICIAL INTELLIGENCE AND DATA SCIENCE FOR COMMUNICATIONS

https://www.comsoc.org/publications/magazines/ieee-communications-magazine/cfp/artificial-intelligence-and-data-science

Please use this link to submit your new manuscript to the Artificial Intelligence and Data Science for Communications Series:

https://ieee.atyponrex.com/journal/COMMAG-IEEE


CALL FOR PAPERS
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The objective of the Artificial Intelligence and Data Science for Communications Series is to provide a forum across industry and academia to advance the development of network and system solutions using data science and artificial intelligence.

Advances of the Internet, mobile and fixed communications, and computing have opened new frontiers for tomorrow’s data-centric society. New applications are increasingly depending on machine-to-machine communications, in turn creating untraditional workloads and demanding more efficient and reliable infrastructures. Such immensely diverse traffic workloads and applications will require dynamic and highly adaptive network environments that are capable of self-optimization for the task at hand while guaranteeing high reliability and ultra-low latency.

Networking devices, sensors, agents, meters, smart vehicles/systems generate tremendous amounts of data while requiring new levels of security, performance, and reliability. Such complexities demand new tools and methodologies for effective services, management, and operation. Predictive network analytics will have an important role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g. prevent network failures, anticipate capacity requirements), and overall decision making throughout the network. Data mining and analytic tools for inferring quality of experience (QoE) signals are needed to improve user experience and service quality.

Innovations in artificial intelligence, machine learning, reinforcement learning and network data analytics introduce new opportunities in various areas, such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi-agent systems, prioritization of network ultra-broadband deployments. These new analytic platforms will help revolutionize our networks and user experience. Through gathering, processing, learning and controlling the vast amounts of information in an intelligent manner future networks will enable unprecedented automation and optimization.

This Series solicits articles addressing numerous topics within its scope including, but not limited to, the following:

All aspects of artificial intelligence, machine learning, reinforcement learning and data analytics aiming at enabling and enhancing next generation networks. The scope of issues that can be addressed includes both conventional measures such as traffic management, QoE, service quality, as well as future network behavior through intelligent services and applications.
Methods, systems and infrastructure for the analysis of network, service traffic and user behavior for efficient and reliable design of networks, including deep learning and statistical methods for network tomography.
Predictive analytics and artificial intelligence for network optimization, network security, network assurance, and data privacy and integrity. Diagnosis of network failures using analytics and AI.
Automated communication infrastructure among smart machines and agents (including humans, e.g. speech and vision), and information fusion for automation and enablement of multi-agent systems.
Communication and networking to facilitate smart data-centric applications

Submission Guidelines

Manuscripts must be submitted through the magazine’s submissions Website, Manuscript Central. You will need to register and then proceed to the author center. On the manuscript details page, please select Artificial Intelligence and Data Science for Communications Series from the drop-down menu. Manuscripts should be tutorial in nature and should not be under review for any other conference or journal. They should be written in a style comprehensible and accessible to readers outside the specialty of the article. Mathematical equations should not be used. For detailed submission guidelines please refer to the magazine website for the list of Manuscript Submission Guidelines that must be followed by all submissions to the IEEE Communications Magazine.

Papers can be submitted anytime during the year. They will receive a review process, and, if accepted, they will be published in the first slot available for this Series.

Sincerely,
the Artificial Intelligence and Data Science for Communications Series Editors

Special Issue on Experimentation in Large-Scale Wireless Community Testbeds
截稿日期: 2024-05-15

The gap between fundamental research in wireless technologies and their real-world testing has been growing rapidly. As the wireless research community pursues the “next big idea”, many discoveries and basic research findings from universities and national labs end up not getting tested in the field. Validating research outcomes via theoretical analysis and computer simulations alone carries the risk of using models that fall short of capturing real-world circumstances, including propagation conditions, waveforms, protocol aspects, and hardware impairments. These shortcomings in such evaluation methodologies may result in misleading conclusions about the real-world performance of new technology. Even when experiments are conducted, they may often be carried out with a narrow geographical and/or technical scope that is not representative of their typical deployment and operational environments, nor can they be reproduced. Many corporations, on the other hand, are often focused on product development and on short-term research agendas with an immediate impact on revenue, and market pressures may not always permit investing in fundamental research studies and their real-world testing. 

To fill this “valley of death” and enable experimentation with advanced wireless technologies at scale, various open, programmable, and remotely accessible wireless community testbeds (WCTs) have been developed in the past years. Rather than being institutional testbeds, these WCTs are meant to serve the experimental needs of the broader wireless research community. For example, in the United States, Platforms for Advanced Wireless Research (PAWR) WCTs have been funded by the National Science Foundation, which includes POWDER, COSMOS, AERPAW, ARA, and Colosseum, and these platforms are accessible to researchers from academia, industry, and government. In Europe, the Fed4Fire project and the new ESFRI SLICES initiative are addressing this objective. IEEE Future Networks initiative is also working on creating a virtual testing platform to accelerate innovations in 5G networks and beyond. Collectively, these WCTs support experiments in a variety of wireless propagation environments including urban, suburban, rural, fixed wireless and high-mobility, and air-to-ground. They also support experiments in various vertical use cases, including but not limited to smart cities, smart agriculture, public safety, vehicular networks, and advanced aerial mobility. Users of these WCTs can often develop and test their experiments initially using a “digital twin” of the actual testbed, which subsequently gets deployed in the real world. In parallel, fueled by the recent advances in Open RAN technologies, various Open Testing and Integration Centers (OTICs) have also been launched across the world, hosted by academic institutions and companies, to support experimentation and testing with next-generation wireless technologies developed by researchers in industry, academia, and government. 

With the WCTs and OTICS reaching fruition in the past years, we have seen an increase in the research carried out in these testbeds. Various workshops have been dedicated to studying experimental results with open-source platforms and at WCTs, including but not limited to ACM WiNTECH Workshop, IEEE INFOCOM CNERT, IEEE Testbeds4Wireless Workshop, GNU Radio Conference, IEEE Globecom FutureG Experimental Test Platforms, workshops organized by srsRAN and OpenAirInterface, workshops organized by the individual PAWR platforms, among others. With this background, this Feature Topic (FT) aims to bring together researchers, industry practitioners, and individuals working on experimentation and research in large-scale WCTs, OTICs, and their digital twins, to share their new ideas, latest findings, and state-of-the-art results. Prospective authors are invited to submit articles on topics including, but not limited to: 

- Experimental results on 5G/6G technologies in WCTs; 
- Large scale emulation experiments in digital twins; 
- Experimental studies at millimeter wave, sub-terahertz, and terahertz bands using WCTs; 
- Experimental studies on spectrum utilization, sharing, coexistence, and radio dynamic zones using WCTs; 
- Experimental studies on fundamental technologies for next generation wireless networks, including but not limited to massive MIMO, new waveforms (e.g., OTFS, FBMC), and new multiple access techniques (e.g., NOMA, RSMA); 
- Experimental and testing results on Open RAN technologies at WCTs and OTICs; 
- Field deployment and performance evaluation of AI/ML techniques using WCTs; 
- Large scale testing of non-cellular technologies including WiFi and LoRa at WCTs; 
- Channel propagation measurements and modeling using WCTs; 
- Experimentation on vertical use case scenarios using WCTs, including but not limited smart cities, smart agriculture, smart grids, public safety, vehicular networks, and drones; 
- Research studies documenting design, development, deployment, and operational challenges of WCTs and OTICs; 
- Current and emerging use cases of WCTs and lessons learned from experimentation with 3GPP, O-RAN, WiFi, and other standards at WCTs; 
- Open research data and reproducibility of experiments; 
- Experiences in configuring WCTs as a general-purpose data generation platform that can serve more than one specific use case.