Machine Learning and Data Analytics for Communication Networks in the 5G Era
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 435
Special Issue Editors
Interests: communication networks; 5G; cloud networks; machine learning; optical fiber networks
Interests: Optical WDM Networks; Performance evaluation of networks; Machine Learning; Big data
Interests: Machine Learning; Optical Networks; Network Control; Energy efficiency
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The next generation (5G) of communication networks will target unprecedented performance, in terms of network capacity, Quality of Service, network availability, user-experience, etc. In this context, the cloud computing and Network Function Virtualization (NFV) paradigms, together with the support of optical networking, play key roles, which are expected to enable 5G networking.
Advanced mathematical tools, such as those in the field of Machine Learning (ML) and Big Data Analytics, also represent an extremely important opportunity to help telecom operators with the design, operation and maintenance of their networks, especially if considering the continuous increase in network complexity. As a matter of fact, thanks to the possibility of efficiently leveraging large amounts of data, ML tools are expected to improve 5G networks through automation and self-optimization.
The scope of this Special Issue is on the most recent applications of ML and Big Data Analytics on the design and operation of next-generation networks. Topics accepted in the Special Issue include (but are not limited to) the following:
- Use of ML and data analytics in the Cloud, Mobile Edge Computing and Data Center Networks
- Network Function Virtualization and Service Function Chaining with ML
- Network telemetry, monitoring and data collection
- Applications of ML to next-generation wired/wireless networks
- ML for 5G-services traffic classification and forecast
- Resource allocation using ML
- Cross-layer network optimization with ML
- Failure prediction, detection and management with ML
- Reinforcement learning for network control
Dr. Francesco Musumeci
Dr. José Alberto Hernández
Dr. Ignacio de Miguel
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- 5G Networking
- Optical Networking
- Machine Learning
- Data Analytics, Cloud and Data Center Networks
- Mobile Edge Computing
- Network Function Virtualization
- Network Cognition
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