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Application of Deep Learning Method in 6G Communication Technology

Topic Information

Dear Colleagues,

Today’s economic growth highly relay on innovation in digital technology. Still, modern society does not fully utilize 5G, but the need for 6G communication is widespread. Deep learning is well utilized in advanced communication infrastructure to offload the data, resource allocation, security, etc. Internet of things in every smart application like vehicles, health care, home, and cities requires deep learning to predict and classify the incoming data accurately. Tasks that require analyzing, collecting, and large data interpretation require deep learning. It makes computation faster similar to human thinking. The dynamic application requires an automatic decision-making system which is possible using deep learning techniques. Next-generation ultra-high networking application requires challenging technology to operate with high reliability. The 6G era with deep learning can be used to communicate in a network with ultra-high latency, ultra-high reliability with low energy consumption. The 6g network must be sufficient to handle big data with sustainable networking. Therefore, big data prediction, big data classification, and data analytics using deep learning techniques give sustainable data management. The smart 6g applications like smart waste management, smart women safety things, smart ecosystem monitoring, etc., require deep supervised learning algorithms. Diseases diagnosis, automatic vehicle routing, and security applications are computed efficiently using deep reinforcement learning. The 6g networking using cloud computing, edge computing, fog computing, and pervasive computing technologies must be approached using deep learning algorithms. This special issue invites innovative ideologies as follows,

  1. Deep learning techniques for ultra-low latency communication using 6G
  2. IoT /IoMT/IIoT ideologies for 6g networks using deep learning models
  3. Mobile communication using 6g architectures and artificial intelligence
  4. Smart women safety applications using deep 6G technologies
  5. Deep learning-based 6G optimization techniques
  6. THz communication using deep learning in 6G
  7. Wireless sensor 6G network
  8. Time, storage cost, computation cost design using deep learning in 6g applications
  9. Smart city, smart healthcare, smart banking in 6G framework
  10. Network security, blockchain, quantum computing in 6G using deep learning
  11. Edge, cloud, and fog computing for managing big data in 6G networks
  12. Data analytics, data science, and Information management using deep learning in 6G
  13. Unmanned Ariel vehicle (UAV), satellite image processing in 6G environment
  14. Blockchainhain, supply chain management using 6G networks.
  15. Industrial 6G applications and underwater 6G surveillance
  16. Defense application enhancement using 6g and deep learning

Dr. Mohamed Abouhawwash
Dr. K. Venkatachalam
Topic Editors

Keywords

  • IoV using 6G networks
  • quantum deep learning models for 6G communication
  • data security and cryptographic techniques for 6G networks
  • image processing techniques for 6G applications
  • 6G agricultural applications

Participating Journals

Data
Open Access
1,220 Articles
Launched in 2016
2.0Impact Factor
5.0CiteScore
25 DaysMedian Time to First Decision
Q2Highest JCR Category Ranking
Future Internet
Open Access
3,061 Articles
Launched in 2009
3.6Impact Factor
8.3CiteScore
17 DaysMedian Time to First Decision
Q2Highest JCR Category Ranking
Information
Open Access
5,272 Articles
Launched in 2010
2.9Impact Factor
6.5CiteScore
19 DaysMedian Time to First Decision
Q2Highest JCR Category Ranking
Mathematics
Open Access
24,607 Articles
Launched in 2013
2.2Impact Factor
4.6CiteScore
18 DaysMedian Time to First Decision
Q1Highest JCR Category Ranking
Symmetry
Open Access
16,069 Articles
Launched in 2009
2.2Impact Factor
5.3CiteScore
17 DaysMedian Time to First Decision
Q2Highest JCR Category Ranking

Published Papers