EVOLUTION OF WIRELESS (CELLULAR) COMMUNICATION NETWORKS
Abstract
Subject and Purpose. One of today’s challenges in contemporary radio physics is exploring the terahertz frequency range which holds immense promise for revolutionary new applications in part at the level of wireless communication systems. A substantial frequency capacity of the range theoretically permits a usable frequency band expansion to a hundred terahertz. The data transfer rate can increase by many orders of magnitude, surpassing the capabilities of current networks. An urgent research priority involves assessing the potential growth rates of wireless communication network resources. The immediate purpose of this paper is to suggest simple mathematical models developed to predict the growth rates of wireless (cellular) communication network resources over the next 20 to 25 years.
Methods and Methodology. The research problem receives analytical consideration, systems analysis, and mathematical modeling of the evolutionary pace of wireless communication in new generations.
Results. Using average data on the parameters of 1G to 5G communication networks and 6G in development, we have built regression models representative of the evolution of information transfer rates and data transmission durations until the year 2050. Equations have been derived describing the evolution of the main parameters of wireless communications. The information rate increase since 1979 is shown to obey the instability equation, wherein the main parameter of the communication system exhibits exponential growth. Models featuring accelerated evolution have been proposed along with more realistic, slowed evolution models considering the saturation effect and a substantial slowdown in the information transfer rates. The saturation effect is associated with the exponential growth of the characteristic evolution time and determined by the data rate growth slowdown, with fundamental, conditionally fundamental, and scientific-technical constraints considered. It has been substantiated that 8G is not expected sooner than 2040—2045 and will likely terminate the wireless communication evolution, with a maximum information transfer rate of 300 to 1000 Tbit/s in the terahertz range.
Conclusions. The mathematical models developed by the authors are simple and capable of predicting the growth dynamics of wireless communication network resources.
Keywords: wireless network; network evolution; data rate; delay time; information transfer duration; regression; saturation effect; fundamental constraint
Manuscript submitted 04.05.2024
Radio phys. radio astron. 2025, 30(2): 089-100
REFERENCES
1. Laiho, J., Wacker, A., and Novosad, T., eds., 2006. Radio Network Planning and Optimisation for UMTS. 2nd ed. John Wiley
& Sons, LTD.
2. Sharma, P., Sharma, D., and Singh, R.K., 2015. Evolution of mobile wireless communication networks (0G−8G). Int. J. App. Eng. Res., 10(6), pp. 14765—14778.
3. Mourad, A., Yang, R., Lehne, P.H., and de la Oliva, A., 2020. Towards 6G: Evolution of Key Performance Indicators and Technology Trends. In: 2020 2nd 6G Wireless Summit (6G SUMMIT). Levi, Finland, 2020, pp. 1—5. DOI: 10.1109/6GSUM MIT49458.2020.9083759
4. Solyman, A.A., and Yahya, Kh., 2022. Evolution of wireless communication networks: from 1G to 6G and future perspective.
Int. J. Electr. Comput. Eng., 12(4), pp. 3943—3950. DOI: 10.11591/ijece.v12i4.pp3943-3950
5. Giribaldi, G., Colombo, L., Simeoni, P., and Rinaldi, M., 2024. Compact and wideband nanoacoustic pass-band filters for future 5G and 6G cellular radios. Nat. Commun., 15, 304. DOI: 10.1038/s41467-023-44038-9
6. Sharma, V. and Nayanam, K., 2024. Sixth Generation (6G) to the Waying Seventh (7G) Wireless Communication Visions and Standards, Challenges, Applications. Int. J. Adv. Res. Sci. Tech., 13(2), pp. 1248—1255. DOI: 10.62226/ijarst20241319
7. Bhatia, S., Mallikarjuna, B., Gautam, D., Gupta, U., Kumar, S., and Verma, S., 2023. The Future IoT: The Current Generation 5G and Next Generation 6G and 7G Technologies. In: 2023 Int. Conf. on Device Intelligence, Computing and Communica- tion Technologies (DICCT). Dehradun, India, pp. 212—217. DOI:10.1109/DICCT56244.2023.10110066
8. Kumar, A., Gaur, N., and Nanthaamornphong, A., 2024. Improving the latency for 5G/B5G based smart healthcare connectivity in rural area. Sci. Rep., 14(1), 6976. DOI: 10.1038/s41598-024-57641-7
9. Basha, P.H., Prathyusha, G., Rao, D.N., Gopikrishna, V., Peddi, P., and Saritha, V., 2023. AI-Driven Multi-Factor Authentication and Dynamic Trust Management for Securing Massive Machine Type Communication in 6G Networks. Int. J. Intel. Sys. Appl. Eng., 12(1S), pp. 361—374. Retrieved from: https://ijisae.org/index.php/IJISAE/article/view/3422. Date of Access: May 03, 2024
10. Yao, Yu., Dong, D., Cai, C., Huang, S., Yuan, X., and Gong, X., 2024. Multi-UAV-assisted Internet of Remote Things communica- tion within satellite–aerial–terrestrial integrated network. EURASIP J. Adv. Signal Process., 10. DOI:10.1186/s13634-023-01101-3
11. Lin, H., Garg, S., Hu, J., Kaddoum, G., Peng, M., and Hossain, M.S., 2021. A Blockchain-Based Secure Data Aggrega- tion Strategy Using Sixth Generation Enabled Network-in-Box for Industrial Applications. IEEE Trans. Ind. Inf., 17(10), pp. 7204—7212. DOI: 10.1109/TII.2020.3035006
12. 2023 IEEE 97th Vehicular Technology Conference: Proc. VTC 2023-Spring. Florence, Italy, 20—23 June 2023.
13. Guan, K., He, D., Ai, B., Chen, Y., Han, C., Peng, B., Zhong, Z., and Kürner, T., 2021. Channel Characterization and Ca- pacity Analysis for THz Communication Enabled Smart Rail Mobility. IEEE Trans. Vehicular Tech., 70(5), pp. 4065—4080. DOI: 10.1109/TVT.2021.3071242
14. Yin, L., Yang, R., and Yao, Yu., 2021. Channel Sounding and Scene Classification of Indoor 6G Millimeter Wave Channel Based on Machine Learning. Electronics, 10(7), 843. DOI: 10.3390/electronics10070843
15. Kumar, A., Gupta, M., Pitchappa, P., Wang, N., Szriftgiser, P., Ducournau, G., and Singh, R., 2022. Phototunable chip-scale topological photonics: 160 Gbps waveguide and demultiplexer for THz 6G communication. Nat. Commun., 13, 5404. DOI: 10.1038/s41467-022-32909-6
16. Liu, Z., Zhang, M., Zhang, C., and Hu, Z., 2023. 6G Network Self-Evolution: Generating Core Networks. In: 2023 IEEE Int. Conf. on Communications Workshops (ICC Workshops). Rome, Italy, 2023, pp. 625—630. DOI: 10.1109/ICCWork- shops57953.2023.10283790
17. Vaigandla, K.K., Azmi, N., Podila, R., and Karne, R.K., 2021. A Survey on Wireless Communications: 6G and 7G. Int. J. Sci., Tech. Manag., 2(6), pp. 2018—2025. DOI: 10.46729/ijstm.v2i6.379
18. Kanno, A., 2023. Seamless Convergence Between Terahertz Radios and Optical Fiber Communication Toward 7G Systems.
IEEE J. Sel. Top. Quantum Electron., 29(5): Terahertz Photonics, pp. 1—9, 8600509. DOI: 10.1109/JSTQE.2023.3311793
19. Rao, A.S., Sreeja Mole, S.S., and Rajeshwar Raju, D.V., 2023. Beyond 5G and 6G: A Comprehensive Overview of 7G Wire- less Communication Technologies. Eur. Chem. Bull., 12(4), pp. 9725—9739. DOI: 10.48047/ecb/2023.12.si4.875
20. Shoewu, O.O., and Ayangbekun Oluwafemi, J., 2020. Insights into the development trends in 7G mobile wireless networks.
J. Adv. Eng. Technol., 8(1), 02. DOI: 10.5281/zenodo.3930583
21. Yuan, Yi., Huang, Yu., and Luo, F.-L., 2024. Metasurfaces for Wireless Communications: Designs and Implementations. 1st ed. Boca Raton: CRC Press. DOI: 10.1201/9781003381259
22. Wang, C. -X., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., Thompson, J.S., Larsson, E.G., Renzo, M.D., Tong, W., Zhu, P., Shen, X., Poor, H.V., Hanzo, L., 2023. On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds. IEEE Commun. Surv. Tutor., 25(2), pp. 905—974. DOI: 10.1109/COMST.2023.3249835
23. Xiu, L., 2022. The Turn of Moore’s Law from Space to Time. Springer Singapore, XXIV, 323 p. DOI: 10.1007/978-981-16- 9065-5
24. Xiu, L., 2019. Time Moore: Exploiting Moore’s Law From The Perspective of Time. IEEE Solid-State Circuits Mag., 11(1), pp. 39—55. DOI: 10.1109/mssc.2018.2882285
Methods and Methodology. The research problem receives analytical consideration, systems analysis, and mathematical modeling of the evolutionary pace of wireless communication in new generations.
Results. Using average data on the parameters of 1G to 5G communication networks and 6G in development, we have built regression models representative of the evolution of information transfer rates and data transmission durations until the year 2050. Equations have been derived describing the evolution of the main parameters of wireless communications. The information rate increase since 1979 is shown to obey the instability equation, wherein the main parameter of the communication system exhibits exponential growth. Models featuring accelerated evolution have been proposed along with more realistic, slowed evolution models considering the saturation effect and a substantial slowdown in the information transfer rates. The saturation effect is associated with the exponential growth of the characteristic evolution time and determined by the data rate growth slowdown, with fundamental, conditionally fundamental, and scientific-technical constraints considered. It has been substantiated that 8G is not expected sooner than 2040—2045 and will likely terminate the wireless communication evolution, with a maximum information transfer rate of 300 to 1000 Tbit/s in the terahertz range.
Conclusions. The mathematical models developed by the authors are simple and capable of predicting the growth dynamics of wireless communication network resources.
Keywords: wireless network; network evolution; data rate; delay time; information transfer duration; regression; saturation effect; fundamental constraint
Manuscript submitted 04.05.2024
Radio phys. radio astron. 2025, 30(2): 089-100
REFERENCES
1. Laiho, J., Wacker, A., and Novosad, T., eds., 2006. Radio Network Planning and Optimisation for UMTS. 2nd ed. John Wiley
& Sons, LTD.
2. Sharma, P., Sharma, D., and Singh, R.K., 2015. Evolution of mobile wireless communication networks (0G−8G). Int. J. App. Eng. Res., 10(6), pp. 14765—14778.
3. Mourad, A., Yang, R., Lehne, P.H., and de la Oliva, A., 2020. Towards 6G: Evolution of Key Performance Indicators and Technology Trends. In: 2020 2nd 6G Wireless Summit (6G SUMMIT). Levi, Finland, 2020, pp. 1—5. DOI: 10.1109/6GSUM MIT49458.2020.9083759
4. Solyman, A.A., and Yahya, Kh., 2022. Evolution of wireless communication networks: from 1G to 6G and future perspective.
Int. J. Electr. Comput. Eng., 12(4), pp. 3943—3950. DOI: 10.11591/ijece.v12i4.pp3943-3950
5. Giribaldi, G., Colombo, L., Simeoni, P., and Rinaldi, M., 2024. Compact and wideband nanoacoustic pass-band filters for future 5G and 6G cellular radios. Nat. Commun., 15, 304. DOI: 10.1038/s41467-023-44038-9
6. Sharma, V. and Nayanam, K., 2024. Sixth Generation (6G) to the Waying Seventh (7G) Wireless Communication Visions and Standards, Challenges, Applications. Int. J. Adv. Res. Sci. Tech., 13(2), pp. 1248—1255. DOI: 10.62226/ijarst20241319
7. Bhatia, S., Mallikarjuna, B., Gautam, D., Gupta, U., Kumar, S., and Verma, S., 2023. The Future IoT: The Current Generation 5G and Next Generation 6G and 7G Technologies. In: 2023 Int. Conf. on Device Intelligence, Computing and Communica- tion Technologies (DICCT). Dehradun, India, pp. 212—217. DOI:10.1109/DICCT56244.2023.10110066
8. Kumar, A., Gaur, N., and Nanthaamornphong, A., 2024. Improving the latency for 5G/B5G based smart healthcare connectivity in rural area. Sci. Rep., 14(1), 6976. DOI: 10.1038/s41598-024-57641-7
9. Basha, P.H., Prathyusha, G., Rao, D.N., Gopikrishna, V., Peddi, P., and Saritha, V., 2023. AI-Driven Multi-Factor Authentication and Dynamic Trust Management for Securing Massive Machine Type Communication in 6G Networks. Int. J. Intel. Sys. Appl. Eng., 12(1S), pp. 361—374. Retrieved from: https://ijisae.org/index.php/IJISAE/article/view/3422. Date of Access: May 03, 2024
10. Yao, Yu., Dong, D., Cai, C., Huang, S., Yuan, X., and Gong, X., 2024. Multi-UAV-assisted Internet of Remote Things communica- tion within satellite–aerial–terrestrial integrated network. EURASIP J. Adv. Signal Process., 10. DOI:10.1186/s13634-023-01101-3
11. Lin, H., Garg, S., Hu, J., Kaddoum, G., Peng, M., and Hossain, M.S., 2021. A Blockchain-Based Secure Data Aggrega- tion Strategy Using Sixth Generation Enabled Network-in-Box for Industrial Applications. IEEE Trans. Ind. Inf., 17(10), pp. 7204—7212. DOI: 10.1109/TII.2020.3035006
12. 2023 IEEE 97th Vehicular Technology Conference: Proc. VTC 2023-Spring. Florence, Italy, 20—23 June 2023.
13. Guan, K., He, D., Ai, B., Chen, Y., Han, C., Peng, B., Zhong, Z., and Kürner, T., 2021. Channel Characterization and Ca- pacity Analysis for THz Communication Enabled Smart Rail Mobility. IEEE Trans. Vehicular Tech., 70(5), pp. 4065—4080. DOI: 10.1109/TVT.2021.3071242
14. Yin, L., Yang, R., and Yao, Yu., 2021. Channel Sounding and Scene Classification of Indoor 6G Millimeter Wave Channel Based on Machine Learning. Electronics, 10(7), 843. DOI: 10.3390/electronics10070843
15. Kumar, A., Gupta, M., Pitchappa, P., Wang, N., Szriftgiser, P., Ducournau, G., and Singh, R., 2022. Phototunable chip-scale topological photonics: 160 Gbps waveguide and demultiplexer for THz 6G communication. Nat. Commun., 13, 5404. DOI: 10.1038/s41467-022-32909-6
16. Liu, Z., Zhang, M., Zhang, C., and Hu, Z., 2023. 6G Network Self-Evolution: Generating Core Networks. In: 2023 IEEE Int. Conf. on Communications Workshops (ICC Workshops). Rome, Italy, 2023, pp. 625—630. DOI: 10.1109/ICCWork- shops57953.2023.10283790
17. Vaigandla, K.K., Azmi, N., Podila, R., and Karne, R.K., 2021. A Survey on Wireless Communications: 6G and 7G. Int. J. Sci., Tech. Manag., 2(6), pp. 2018—2025. DOI: 10.46729/ijstm.v2i6.379
18. Kanno, A., 2023. Seamless Convergence Between Terahertz Radios and Optical Fiber Communication Toward 7G Systems.
IEEE J. Sel. Top. Quantum Electron., 29(5): Terahertz Photonics, pp. 1—9, 8600509. DOI: 10.1109/JSTQE.2023.3311793
19. Rao, A.S., Sreeja Mole, S.S., and Rajeshwar Raju, D.V., 2023. Beyond 5G and 6G: A Comprehensive Overview of 7G Wire- less Communication Technologies. Eur. Chem. Bull., 12(4), pp. 9725—9739. DOI: 10.48047/ecb/2023.12.si4.875
20. Shoewu, O.O., and Ayangbekun Oluwafemi, J., 2020. Insights into the development trends in 7G mobile wireless networks.
J. Adv. Eng. Technol., 8(1), 02. DOI: 10.5281/zenodo.3930583
21. Yuan, Yi., Huang, Yu., and Luo, F.-L., 2024. Metasurfaces for Wireless Communications: Designs and Implementations. 1st ed. Boca Raton: CRC Press. DOI: 10.1201/9781003381259
22. Wang, C. -X., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., Thompson, J.S., Larsson, E.G., Renzo, M.D., Tong, W., Zhu, P., Shen, X., Poor, H.V., Hanzo, L., 2023. On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds. IEEE Commun. Surv. Tutor., 25(2), pp. 905—974. DOI: 10.1109/COMST.2023.3249835
23. Xiu, L., 2022. The Turn of Moore’s Law from Space to Time. Springer Singapore, XXIV, 323 p. DOI: 10.1007/978-981-16- 9065-5
24. Xiu, L., 2019. Time Moore: Exploiting Moore’s Law From The Perspective of Time. IEEE Solid-State Circuits Mag., 11(1), pp. 39—55. DOI: 10.1109/mssc.2018.2882285
Keywords
wireless network; network evolution; data rate; delay time; information transfer duration; regression; saturation effect; fundamental constraint
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