Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 34, 11 July 2024


Open Access | Article

The development of multicarrier transmission and multiple access methods

Yunkai Hu * 1 , Weiyi Bian 2 , Xuan Xuan 3 , Ruike Wu 4
1 Northeastern University
2 Northeastern University at Qinhuangdao
3 Hangzhou No.14 Secondary School
4 Fuzhou University

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 34, 351-359
Published 11 July 2024. © 11 July 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yunkai Hu, Weiyi Bian, Xuan Xuan, Ruike Wu. The development of multicarrier transmission and multiple access methods. TNS (2024) Vol. 34: 351-359. DOI: 10.54254/2753-8818/34/20240754.

Abstract

This paper presents an overview of development of multicarrier transmission and multiple access methods over the past decades. Focus shifts towards optimizing communication methods for a more expeditious, reliable, and efficient system. Several communicational techniques, including TDMA, CDMA, OFDMA, and GFDMA are discussed. With the growing trend of artificial intelligence, it can be predicted that artificial intelligence will be of great use in improving multicarrier transmission and multiple access methods.

Keywords

Multicarrier, Multiple Access, Communicational Techniques, Artificial Intelligence

References

1. L. Coe, in The telegraph: A history of Morse’s invention and its predecessors in the United States, Jefferson, NC: McFarland & Company, 2003, pp. 26–37.

2. J. C. Maxwell, in A dynamical theory of the electromagnetic field, London: The Society, 1865, pp. 459–512.

3. P. K. Bondyopadhyay, “Guglielmo Marconi - the father of Long Distance Radio Communication - An Engineer’s tribute,” 25th European Microwave Conference, 1995, 1995. doi:10.1109/euma.1995.337090.

4. G. S. Vernam, “Cipher Printing Telegraph Systems: For secret wire and Radio T elegraphic Communications,” Journal of the A.I.E.E., vol. 45, no. 2, pp. 109–115, 1926. doi:10.1109/jaiee.1926.6534724.

5. R. Fantacci and S. Nannicini, “Performance evaluation of a reservation TDMA protocol for voice/data transmission in personal communication networks with Nonindependent Channel Errors,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 9, pp. 1636–1646, 2000. doi:10.1109/49.872952.

6. K. Benkic, “Proposed use of a CDMA technique in wireless sensor networks,” 2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, 2007. doi:10.1109/iwssip.2007.4381112.

7. C. K. Kwabi et al., “Operational advantages of the AT&T CDMA cellular system,” [1992 Proceedings] Vehicular Technology Society 42nd VTS Conference - Frontiers of Technology. Denver, CO, USA, 1992, pp. 233-235 vol.1, doi: 10.1109/VETEC.1992.245433.

8. J. G. Andrews et al., “What Will 5G Be?,” in IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065-1082, June 2014, doi: 10.1109/JSAC.2014.2328098.

9. S. Sesia, I. Toufik, and M. Baker, “Orthogonal Frequency Division Multiple Access (OFDMA),” in LTE - the UMTS long term evolution: From theory to practice, Hoboken, New Jersey: John Wiley & Sons, 2015, pp. 113–134.

10. E. Dahlman, S. Parkvall, and J. Sköld, “Overall Transmission Structure,” in 5G nr: The next generation wireless access technology, London: Elsevier, Academic Press, 2021, pp. 115–145.

11. W. Jing, Z. Lu, X. Wen, Z. Hu, and S. Yang, “Flexible resource allocation for joint optimization of energy and spectral efficiency in OFDMA Multi-Cell Networks,” IEEE Communications Letters, vol. 19, no. 3, pp. 451–454, 2015. doi:10.1109/lcomm.2015.2392113.

12. J. Fu and Y. Karasawa, “Fundamental analysis on throughput characteristics of orthogonal frequency division multiple access (OFDMA) in multipath propagation environments,” Proceedings IEEE 56th Vehicular Technology Conference, 2002. doi:10.1109/vetecf.2002.1040802.

13. M. Konstantinos, A. Adamis, and P. Constantinou, “SNR degradation due to timing and frequency synchronization errors for OFDMA systems with Subband Carrier Allocation,” 2008 14th European Wireless Conference, 2008. doi:10.1109/ew.2008.4623899.

14. M. Morelli, C.-C. J. Kuo, and M.-O. Pun, “Synchronization techniques for orthogonal frequency division multiple access (OFDMA): A Tutorial Review,” Proceedings of the IEEE, vol. 95, no. 7, pp. 1394–1427, 2007. doi:10.1109/jproc.2007.897979.

15. L. Cimini, “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Transactions on Communications, vol. 33, no. 7, pp. 665–675, 1985. doi:10.1109/tcom.1985.1096357.

16. I. Jagenneevas and P. Dananjayan, “Performance analysis of GFDMA and SC-FDMA in Rayleigh and Gaussian Fading Channel,” 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), 2015. doi:10.1109/isco.2015.7282342.

17. M. Shafi et al., “5G: A tutorial overview of standards, trials, challenges, deployment, and Practice,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, pp. 1201–1221, 2017. doi:10.1109/jsac.2017.2692307.

18. S. Zhao, Y. Fang and L. Qiu, “Deep Learning-Based channel estimation with SRGAN in OFDM Systems,” 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 2021, pp. 1-6, doi: 10.1109/WCNC49053.2021.9417242.

19. R. Yao, S. Wang, X. Zuo, J. Xu and N. Qi, “Deep Learning Aided Signal Detection in OFDM Systems with Time-Varying Channels,” 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Victoria, BC, Canada, 2019, pp. 1-5, doi: 10.1109/PACRIM47961.2019.8985060.

20. T. Fathi, S. Ashraf and A. A. Rhebi, “Deep Learning For Channel Estimation And Signal Detection,” 2022 International Conference on Engineering & MIS (ICEMIS), Istanbul, Turkey, 2022, pp. 1-6, doi: 10.1109/ICEMIS56295.2022.9914039.

21. J. Ruseckas, G. Molis, A. Mackutė-Varoneckienė and T. KrilaviČius, “Multi-carrier Signal Detection using Convolutional Neural Networks,” 2019 International SoC Design Conference (ISOCC), Jeju, Korea (South), 2019, pp. 190-191, doi: 10.1109/ISOCC47750.2019.9078534.

22. J. Cao, C. Liu and Y. Lu, “Performance analysis of AI aided embedded OFDM receiver based on RK3399 platform,” 2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), Hangzhou, China, 2021, pp. 64-69, doi: 10.1109/ICWCSG53609.2021.00020.

23. Z. Kaleem, M. Ali, I. Ahmad, W. Khalid, A. Alkhayyat and A. Jamalipour, “Artificial Intelligence-Driven Real-Time Automatic Modulation Classification Scheme for Next-Generation Cellular Networks,” in IEEE Access, vol. 9, pp. 155584-155597, 2021, doi: 10.1109/ACCESS.2021.3128508.

24. T. Huynh-The et al., “Automatic Modulation Classification: A Deep Architecture Survey,” in IEEE Access, vol. 9, pp. 142950-142971, 2021, doi: 10.1109/ACCESS.2021.3120419.

25. J. Nie, Y. Zhang, Z. He, S. Chen, S. Gong and W. Zhang, “Deep Hierarchical Network for Automatic Modulation Classification,” in IEEE Access, vol. 7, pp. 94604-94613, 2019, doi: 10.1109/ACCESS.2019.2928463.

26. T. Huynh-The, C. -H. Hua, Q. -V. Pham and D. -S. Kim, “MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification,” in IEEE Communications Letters, vol. 24, no. 4, pp. 811-815, April 2020, doi: 10.1109/LCOMM.2020.2968030.

27. P. Yu, F. Zhou, X. Zhang, X. Qiu, M. Kadoch and M. Cheriet, “Deep Learning-Based Resource Allocation for 5G Broadband TV Service,” in IEEE Transactions on Broadcasting, vol. 66, no. 4, pp. 800-813, Dec. 2020, doi: 10.1109/TBC.2020.2968730.

28. W. Wu, F. Yang, F. Zhou, Q. Wu and R. Q. Hu, “Intelligent Resource Allocation for IRS-Enhanced OFDM Communication Systems: A Hybrid Deep Reinforcement Learning Approach,” in IEEE Transactions on Wireless Communications, vol. 22, no. 6, pp. 4028-4042, June 2023, doi: 10.1109/TWC.2022.3222864.

29. Y. Guo, F. -C. Zheng, J. Luo and X. Wang, “Optimal Resource Allocation via Machine Learning in Coordinated Downlink Multi-Cell OFDM Networks under High Mobility,” 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9448996.

30. Y. Guo, F. -C. Zheng, J. Luo and X. Wang, “Optimal Resource Allocation via Machine Learning in Coordinated Downlink Multi-Cell OFDM Networks under Imperfect CSI,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-6, doi: 10.1109/VTC2020-Spring48590.2020.9128768.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
ISBN (Print)
978-1-83558-369-2
ISBN (Online)
978-1-83558-370-8
Published Date
11 July 2024
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/34/20240754
Copyright
11 July 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated