ML-Based Cognitive Network Management: For Better 6G Applications

Dear Colleagues,

A cognitive network (CN) is a system that employs cognitive processes to analyze the current condition, make a decision based on such findings, and then improve from such actions. 5G cognitive radio networks (CRNs) are crucial in global communication and spectrum sharing. Massive machine-type communication, increased mobile internet, and latency sufficiently high levels of connectivity are the fundamental components of 5G Cognitive radio (CR) communication. AI and ML are the fastest-growing and most-demanded information and communication technology development approaches. 6G wireless networks are dependable connectivity to allow enormous data transmission at various frequencies and employ a wide range of technologies.

In general, AI and Machine Learning (ML) may help to realize and optimize 6G network applications. The use of machine learning techniques in 6G wireless communication networks has stimulated concern. Cognitive or flexible spectrum allocation systems enable intelligent, adaptive wireless connections that coexist with existing wireless networks and allow access anytime, anywhere. Cognitive radio devices constantly monitor their surroundings and access spectral range non-intrusive, decentralized manner. CN would optimize frequency allocation and design a cognitive radio network using AI. CN techniques can also be used for mobile computing to provide computational assistance for iPhone applications. The CR network is given the cognitive ability to sense and gather information from their surroundings and the reconfigurability to modify operational parameters in response to the sensed data quickly. Machine learning-based algorithms and models can help with wireless network analysis and resource management and handle the growing volume of communication and processing emerging networking applications require.

It easily handles the current challenges of communications systems. The cognitive network employs powerful analytical methods from multiple study disciplines. To increase capacity, minimize latency, and improve spectrum splitting, the technology makes better use of decentralized radio access network (RAN) and the terahertz (THz) spectrum. 6G CR networks are expected to bring novel application cases and performance measures, such as global coverage, efficiency, increased spectrum, energy intelligence, and safety. Analyzing data to construct or build networks normally occurs in Machine Learning. One of the major challenges that machine learning experts deal with is a lack of quality information. We're searching for papers highlighting ML-based cognitive network management for better generation and unique features that will enable developing technology and make the generation safer and wealthier.

Topics of  interest include but are not limited to:

1. Setting the Future of Wireless Powered Network using AI;
2. The Coming Rise of Machine Network Management Based on IoT;
3. Insights Cognitive Network management using AI;
4. Envisioning AI for Communication and Networking;
5. Exploring Communication and Network Resources based on IoT;
6. Trends and Focus of AI Application using IoT Edge;
7. New Technologies for Networking Using IoT;
8. New Advances in ML-based Data Analysis for 6G Network;
9. The Importance of Wireless Power Transfer in 6G Network;
10. Smart Practices and Technologies for Cognitive Aspects using AI;
11. Pushing the Boundaries of 6G Communication Network  using AI;
12. Wireless Communication Network Management: The Road Towards 6G.

Dr. Mohammad Kamrul Hasan
Dr. Nazmus Shaker Nafi
Dr. Simar Preet Singh
Guest Editors

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