Machine Learning and Cognitive Networking
A special issue of Telecom (ISSN 2673-4001).
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 22059
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid progress of cloud with high bitrate requirements substantially affects transport networks. To overcome the issue of capacity crunch in transport networks, new cognitive models need to be developed. These new models are needed to extract valuable information from a comprehensive set of network data. A cognitive network utilizes advanced analytical solutions from several research areas (i.e., deep learning, data analytics, knowledge representation, telecommunication, network management) to solve modern problems in communication networks. The cognitive processes, which learn or use historical data to improve performance, apply various data analytics solutions typically utilizing machine learning techniques. In particular, data analytics (DA), machine learning (ML), and deep learning (DL) concepts are regarded as promising methodological areas to enable cognitive network data analysis; thus enabling, for example, automatized network self-configuration and fault management.
This Special Issue solicits the submission of high-quality and unpublished papers that aim to solve open technical problems and challenges related to cognitive networking. Both theoretical and experimental studies are encouraged, as well as high-quality review and survey papers.
Dr. Michał Aibin
Guest Editor
Manuscript Submission Information
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Keywords
- Machine learning and artificial intelligence (AI) for communications and networking
- Machine learning for communication and network resource optimization
- Machine learning for networking using the internet of things
- Distributed learning, reasoning, and optimization for communications and networking
- Architecture, protocols, cross-layer, and cognition cycle design for intelligent communications and networking
- Machine learning for next-generation wireless networks such as 5G and 6G networks
- Security and privacy issues in intelligent communications and networking
- Network tools, testbeds, and performance evaluation based on AI and machine learning
- Data-driven traffic prediction for cognitive network
- AI-based performance evaluation.
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