Chapter 12
Harmonized Mobile Networks and Extreme Global Network Coverage
Chamitha de Alwis,
Quoc-Viet Pham, Madhusanka Liyanage,
Chamitha de Alwis
University of Bedfordshire, Luton, United Kingdom
Search for more papers by this authorBook Author(s):Chamitha de Alwis,
Quoc-Viet Pham, Madhusanka Liyanage,
Chamitha de Alwis
University of Bedfordshire, Luton, United Kingdom
Search for more papers by this authorFirst published: 25 November 2022
Summary
Harmonized mobile networks refer to the smooth interconnection of multiple communication technologies, data storage, and processing platforms at different scales. Extreme global network coverage refers to the digital inclusion through seamless global service coverage by connecting remote, rural, deep-sea, and even space locations. This chapter focuses on the harmonized mobile networks in sixth-generation (6G). It examines the extreme global coverage supported by 6G wireless systems. To enable harmonized networks, 6G should utilize new, advanced networking technologies, such as autonomous mesh networks, IoBNT, IoNT, and Internet of Space Things, scalable device-to-device communications in addition to legacy wireless technologies. Among potential technological solutions, nonterrestrial networks are needed in 6G to achieve global service coverage by offering full digital inclusion in the areas, such as the deep sea and space. To be more specific, a nonterrestrial network is composed of different platforms, from low-altitude platform and high-altitude platform to satellites.
References
- E. Yaacoub and M.-S. Alouini , “ A key 6G challenge and opportunity–connecting the base of the pyramid: A survey on rural connectivity ,” Proceedings of the IEEE , vol. 108 , no. 4 , pp. 533 – 582 , 2020 .
- Q.-V. Pham , F. Fang , V. N. Ha , M. J. Piran , M. Le , L. B. Le , W.-J. Hwang , and Z. Ding , “ A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art ,” IEEE Access , vol. 8 , pp. 116 974 – 117 017 , 2020 .
- I. F. Akyildiz , C. Han , and S. Nie , “ Combating the distance problem in the millimeter wave and Terahertz frequency bands ,” IEEE Communications Magazine , vol. 56 , no. 6 , pp. 102 – 108 , 2018 .
- M. A. Khalighi and M. Uysal , “ Survey on free space optical communication: A communication theory perspective ,” IEEE Communication Surveys and Tutorials , vol. 16 , no. 4 , pp. 2231 – 2258 , 2014 .
- N.-N. Dao , Q.-V. Pham , N. H. Tu , T. T. Thanh , V. N. Q. Bao , D. S. Lakew , and S. Cho , “ Survey on aerial radio access networks: Toward a comprehensive 6G access infrastructure ,” IEEE Communication Surveys and Tutorials , vol. 23 , no. 2 , pp. 1193 – 1225 , 2021 .
- Q.-V. Pham , R. Ruby , F. Fang , D. C. Nguyen , Z. Yang , M. Le , Z. Ding , and W.-J. Hwang , “ Aerial computing: A new computing paradigm, applications, and challenges ,” IEEE Internet of Things Journal , vol. 9 , pp. 8339 – 8363 , 2022 .
- T. R. Gadekallu , Q.-V. Pham , D. C. Nguyen , P. K. R. Maddikunta , N. Deepa , B. Prabadevi , P. N. Pathirana , J. Zhao , and W.-J. Hwang , “ Blockchain for edge of things: Applications, opportunities, and challenges ,” IEEE Internet of Things Journal , vol. 9 , no. 2 , pp. 964 – 988 , 2022 .
- T. Huynh-The , Q.-V. Pham , T.-V. Nguyen , and D.-S. Kim , “Deep learning for coexistence radar-communication waveform recognition,” in 2021 International Conference on Information and Communication Technology Convergence (ICTC) IEEE, 2021 , pp. 1725 – 1727 .
- S. Chen , Y.-C. Liang , S. Sun , S. Kang , W. Cheng , and M. Peng , “ Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed ,” IEEE Wireless Communications , vol. 27 , no. 2 , pp. 218 – 228 , 2020 .
- Q. Huang , M. Lin , W.-P. Zhu , J. Cheng , and M.-S. Alouini , “ Uplink massive access in mixed RF/FSO satellite-aerial-terrestrial networks ,” IEEE Transactions on Communications , vol. 69 , no. 4 , pp. 2413 – 2426 , 2021 .
- X. Wu , M. D. Soltani , L. Zhou , M. Safari , and H. Haas , “ Hybrid LiFi and WiFi networks: A survey ,” IEEE Communication Surveys and Tutorials , vol. 23 , no. 2 , pp. 1398 – 1420 , 2021 .
- S. Szott , K. Kosek-Szott , P. Gawłowicz , J. T. Gómez , B. Bellalta , A. Zubow , and F. Dressler , “ WiFi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning ,” arXiv preprint arXiv:2109.04786 , 2021 .
- L. T. Tan and R. Q. Hu , “ Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning ,” IEEE Transactions on Vehicular Technology , vol. 67 , no. 11 , pp. 10 190 – 10 203 , 2018 .
- L. Huang , S. Bi , and Y.-J. A. Zhang , “ Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks ,” IEEE Transactions on Mobile Computing , vol. 19 , no. 11 , pp. 2581 – 2593 , 2020 .
- Y. He , F. R. Yu , N. Zhao , and H. Yin , “ Secure social networks in 5G systems with mobile edge computing, caching, and device-to-device communications ,” IEEE Wireless Communications , vol. 25 , no. 3 , pp. 103 – 109 , 2018 .
- Z. Liang , H. Chen , Y. Liu , and F. Chen , “ Data sensing and offloading in edge computing networks: TDMA or NOMA? ” IEEE Transactions on Wireless Communications , vol. 21 , pp. 4497 – 4508 , 2022 .
- A. Bordetsky , C. Glose , S. Mullins , and E. Bourakov , “Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise,” in Proceedings of the 52nd Hawaii International Conference on System Sciences , 2019 .
- I. F. Akyildiz , A. Kak , and S. Nie , “ 6G and beyond: The future of wireless communications systems ,” IEEE Access , vol. 8 , pp. 133 995 – 134 030 , 2020 .
- S. Zhang , J. Liu , H. Guo , M. Qi , and N. Kato , “ Envisioning device-to-device communications in 6G ,” IEEE Network , vol. 34 , no. 3 , pp. 86 – 91 , 2020 .
- R. Xie , Q. Tang , S. Qiao , H. Zhu , F. R. Yu , and T. Huang , “ When serverless computing meets edge computing: Architecture, challenges, and open issues ,” IEEE Wireless Communications , vol. 28 , no. 5 , pp. 126 – 133 , 2021 .
- I. Philbeck , “ Connecting the unconnected: Working together to achieve connect 2020 agenda targets ,” ITU White Paper , 2017 .
- M. S. Alam , G. K. Kurt , H. Yanikomer oglu , P. Zhu , and N. D. Dào , “ High altitude platform station based super macro base station constellations ,” IEEE Communications Magazine , vol. 59 , no. 1 , pp. 103 – 109 , 2021 .
- G. K. Kurt , M. G. Khoshkholgh , S. Alfattani , A. Ibrahim , T. S. Darwish , M. S. Alam , H. Yanikomeroglu , and A. Yongacoglu , “ A vision and framework for the high altitude platform station (HAPS) networks of the future ,” IEEE Communication Surveys and Tutorials , vol. 23 , no. 2 , pp. 729 – 779 , 2021 .
- P. K. R. Maddikunta , S. Hakak , M. Alazab , S. Bhattacharya , T. R. Gadekallu , W. Z. Khan , and Q.-V. Pham , “ Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges ,” IEEE Sensors Journal , vol. 21 , no. 16 , pp. 17 608 – 17 619 , 2021 .
- V. Sharma , M. Bennis , and R. Kumar , “ UAV-assisted heterogeneous networks for capacity enhancement ,” IEEE Communications Letters , vol. 20 , no. 6 , pp. 1207 – 1210 , 2016 .
- U. Siddique , H. Tabassum , E. Hossain , and D. I. Kim , “ Wireless backhauling of 5G small cells: Challenges and solution approaches ,” IEEE Wireless Communications , vol. 22 , no. 5 , pp. 22 – 31 , 2015 .
- L. Wei , S. Zhao , O. F. Bourahla , X. Li , F. Wu , Y. Zhuang , J. Han , and M. Xu , “ End-to-end video saliency detection via a deep contextual spatiotemporal network ,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32 , no. 4 , pp. 1691 – 1702 , 2021 .
- M. Mozaffari , W. Saad , M. Bennis , Y.-H. Nam , and M. Debbah , “ A tutorial on UAVs for wireless networks: applications, challenges, and open problems ,” IEEE Communication Surveys and Tutorials , vol. 21 , no. 3 , pp. 2334 – 2360 , 2019 .
