Zero Forcing of Computer Based GSM Bandwidth Optimization
Keywords:
wireless networks, GSM, 4G, Bandwidth optimization control protocol, Digital Data
Abstract
The bandwidth optimization control protocol allows the mobile GSM system to send the information (request) and get the response using two different networks simultaneously. The communication could be provided to the mobile nodes not only by improving the transport layer channel capacity, but also by addressing the problems of the transmission channels and reducing their effects as well eliminating the interference in the Internet layer. This accomplished by using the control protocol technology (Zero Forcing) to improve the bandwidth, options and processes that ensure the correct delivery of request and response information. . In this paper, MBOCP Modified Bandwidth Optimization Control Protocol was introduced using hybrid zero forcing algorithm HZFA. The results of our simulation show that the mobile communication system gets higher and more accurate data rates and uses network resources efficiently compared to using a regular network.
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References
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3. F. Farid, S. Shahrestani, and C. Ruan, “Quality of service concerns in wireless and cellular networks,” Communications of the IBIMA, vol. 2013, pp. 1 – 15, 2013
4. Z. Afzal, P. A. Shah, K. M. Awan, and Zahoor ur Rehman, “Optimum bandwidth allocation in wireless networks using differential evolution,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 4, pp. 1401 – 1412, 2019.
5. J. Chang and H. Chen, "Dynamic-grouping bandwidth reservation scheme for multimedia wireless networks," IEEE Journal on Selected Areas in Communications, vol. 21, no. 10, pp. 1566-1574, 2003
6. B.I. Bakare and T.J. Alalibo, “Gigabit Fidelity (GI-FI) as future wireless technology in Nigeria,” International Journal of Engineering Science Invention, vol. 7, no. 12, ver.3, pp. 1-6, 2018.
7. S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
8. Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief. (2018) “LORA: Learning to Optimize for Resource Allocation in wireless networks with few training samples,” [online] arXiv preprint arXiv: 1812.07998. Available: https://arxiv.org/abs/1812.07998
9. P. Akilandeswari, P. Gopikannan, and C. Balasubramanian, “A bandwidth allocation using genetic algorithm to providing solution for optimization problem in wireless mesh network,” International Journal of Innovation and Scientific Research, vol. 25, no. 2, pp. 445 – 456, 2016.
10. M. L. Umashankar, and M. V. Ramakrishna, “Optimization techniques in wireless sensor networks: A survey,” International Journal of Scientific Research Engineering & Technology, vol 6, no. 7, pp. 704 – 708, 2017.
11. L. Wang, W. Wu, J. Qi, and Z. Jia, “Wireless sensor network coverage optimization based on whale group algorithm,” Computer Science and Information Systems, vol. 15, no. 3, pp. 569-583, 2018
12. C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98- 105, 2017.
13. G. J. Foschini and M. J. Gans, “On Limits of Wireless Personal Communications in a Fading Environment when Using Multiple Antennas,” Wireless Personal Communications, vol. 6, pp. 311–335, March 1998.
14. P.Wolniansky, G. Foschini, G. Golden, and R. Valenzuela, “V-BLAST: An architecture for Realizing Very High Data Rates Over the Rich Scattering Wireless Channel,” in Proceedings of ISSSE-98, IEEE, September 1998.
15. G. Caire and S. Shamai, “On the Achievable Throughput of a Multi-Antenna Gaussian Broadcast Channel.” Submitted to IEEE Transactions on Information Theory, July 2001.
16. P. Viswanath and D. Tse, “Sum Capacity of the Multiple Antenna Gaussian Broadcast Channel and Uplink-Downlink Duality.” Submitted to IEEE Transactions on Information Theory, July 2002.
17. W. Yu and J. Cioffi, “Sum Capacity of Gaussian Vector Broadcast Channels.” Submitted to IEEE Transactions on Information Theory, November 2001.
18. M. Costa, “Writing on Dirty Paper,” IEEE Transactions on Information Theory, vol. 29, pp. 439–441, May 1983.
19. M. Schubert and H. Boche, “An Efficient Algorithm for Optimum Joint Beamforming and Power Control,” in Proceedings of the IEEE Vehicular Technology Conference, vol. 4, pp. 1911–1915, IEEE, Spring 2002.
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23. K.-K. Wong, R. D. Murch, R. S.-K. Cheng, and K. B. Letaief, “Optimizing the Spectral Efficiency of Multiuser MIMO Smart Antenna Systems,” in Proceedings of theWireless Communications and Networking Conference, vol. 1, pp. 426–430, IEEE, 2000.
24. J.-H. Chang, L. Tassiulas, and F. Rashid-Farrokhi, “Joint Transmitter Receiver Diversity for Efficient Space Division Multiaccess,” IEEE Transactions on Wireless Communications, vol. 1, pp. 16–27, January 2002.
25. W. Qiu, H. Tr¨oger, and M. Meurer, “Joint Transmission (JT) in Multi-user MIMO Transmission Systems,” in COST 273 TD(02)008, EURO-COST, January 2002.
Published
2023-11-03
How to Cite
Suham A. Albderi. (2023). Zero Forcing of Computer Based GSM Bandwidth Optimization. Central Asian Journal of Theoretical and Applied Science, 4(11), 1-18. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1330
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Articles
