Skip to main content
Log in

Efficient data transmission over 5G Networks with improved accuracy using 802.11p

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract  

High-quality data traffic management providing ultra-low latency and less circuit complexity are the technical challenges for 5G cellular networks in recent years. To address the increased data rate traffic and enhance the user experience, buffer management with effective utilization of resources are 5G networks. The existing research contains uncompressed or raw video data transmission over a double buffer system. The spectrum is always busy with information if uncompressed data is sent. Transmission delays occur while packet transmission and receiver systems result in video buffering. The authors proposed an optimized resource framework in this research paper by compressing data using a modified H.265 Lagrangian Encoder and transmitting data using a single buffer technique. The transmission delays are mitigated, and data buffering is minimized with reduced transmission errors. The proposed method is tested and verified with various errors like collision error, propagation error, sensing error, and accuracy. The proposed model gives an improvement in accuracy when compared with the existing model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

This manuscript has no associated data.

References

  1. Hussein HH, Elsayed HA, Abd El-kader SM (2020) Intensive benchmarking of D2D communication over 5G cellular networks: prototype, integrated features, challenges, and main applications. Wirel Netw 26:3183–3202

    Article  Google Scholar 

  2. Liang L, Li GY, Xu W (2017) Resource allocation for D2D-enabled vehicular communications. IEEE Trans Commun 65(7):3186–3197

    Article  Google Scholar 

  3. Burger V, Zinner T, Dinh-Xuan L, Wamser F, Tran-Gia P (2018) A generic approach to video buffer modeling using discrete-time analysis. ACM Trans Multimedia Comput Commun Applic (TOMM)s 14(2s):1–23

    Article  Google Scholar 

  4. Roy A, Pachuau JL, Saha AK (2021) An overview of queuing delay and various delay based algorithms in networks. Computing 103(10):2361–2399

    Article  MathSciNet  Google Scholar 

  5. Bisdikian C, Lew JS, Tantawi AN (1996) The generalized D [X]/D/1 queue: A flexible computer communications model. Telecommun Syst 6:127–146

    Article  Google Scholar 

  6. Islam S, Budati AK, Mohammad KH, Goyal SB, Raju D (2023) A multi-sensory real-time data transmission method with sustainable and robust 5G energy signals for smart cities. Sustain Energy Technol Assess 57:103278

    Google Scholar 

  7. Zhang P, Zhao L, Cheng B, Gao P (2022) Optimization of the quality-to-power ratio of scalable video code video transmission in millimeter-wave massive multiple-input multiple-output systems. Trans Emerg Telecommun Technol 33(1):e4379

    Article  Google Scholar 

  8. Vivekananda GN, Reddy PC (2023) Efficient video transmission technique using clustering and optimisation algorithms in MANETs. Int J Adv Intell Paradigms 25(3–4):248–263

    Google Scholar 

  9. Ma C, Chung W (2022) Visual communication design based on collaborative wireless communication video transmission. J Sensors 2022:1–11

    Google Scholar 

  10. Zhang B, Cosman P, Milstein LB (2019) Energy optimization for wireless video transmission employing hybrid ARQ. IEEE Trans Veh Technol 68(6):5606–5617

    Article  Google Scholar 

  11. Nie H, Jiang X, Tang W, Zhang S, Dou W (2020) Data security over wireless transmission for enterprise multimedia security with fountain codes. Multimedia Tools Appl 79:10781–10803

    Article  Google Scholar 

  12. Ramesh S, Yaashuwanth C (2020) Enhanced approach using trust-based decision making for secured wireless streaming video sensor networks. Multimedia Tools Appl 79(15–16):10157–10176

    Article  Google Scholar 

  13. Marinšek A, Van der Perre L (2021) Keeping up with the bits: tracking physical layer latency in millimeter-wave Wi-Fi networks. Netw Internet Arch. https://doi.org/10.48550/arXiv.2105.13147

  14. O’shea T, Hoydis J (2017) An introduction to deep learning for the physical layer. IEEE Trans Cogn Commun Netw 3(4):563–575

    Article  Google Scholar 

  15. Busari SA, Khan MA, Huq KMS, Mumtaz S, Rodriguez J (2019) Millimetre-wave massive MIMO for cellular vehicle-to-infrastructure communication. IET Intel Transp Syst 13(6):983–990

    Article  Google Scholar 

  16. Wang J, Weitzen J, Bayat O, Sevindik V, Li M (2019) Interference coordination for millimeter wave communications in 5G networks for performance optimization. EURASIP J Wirel Commun Netw 2019:1–16

    Article  Google Scholar 

  17. Mezzavilla M, Zhang M, Polese M, Ford R, Dutta S, Rangan S, Zorzi M (2018) End-to-end simulation of 5G mmWave networks. IEEE Commun Surv Tutorials 20(3):2237–2263

    Article  Google Scholar 

  18. Shilpa B, Budati AK, Rao LK, Goyal SB (2022) Deep learning based optimised data transmission over 5G networks with Lagrangian encoder. Comput Electr Eng 102:108164

    Article  Google Scholar 

  19. Fu Y, Wang S, Wang CX, Hong X, McLaughlin S (2018) Artificial intelligence to manage network traffic of 5G wireless networks. IEEE Network 32(6):58–64

    Article  Google Scholar 

  20. Sepulcre M, Gonzalez-Martin M, Gozalvez J, Molina-Masegosa R, Coll-Perales B (2021) Analytical models of the performance of IEEE 802.11 p vehicle to vehicle communications. IEEE Trans Veh Technol 71(1):713–724

    Article  Google Scholar 

  21. Golaghazadeh F, Coulombe S, Robert JM (2022) Residual packet loss rate analysis of 2-D parity forward error correction. Signal Process: Image Commun 102:116597

    Google Scholar 

  22. Tahir MN, Katz M (2022) Performance evaluation of IEEE 802.11 p, LTE and 5G in connected vehicles for cooperative awareness. Eng Rep 4(4):e12467

    Article  Google Scholar 

  23. Li S, Li H, Gaber J, Yang S, Yang Q (2023) Performance Analysis of IEEE 802.11 p Protocol in IoV under Error-Prone Channel Conditions. Sec Commun Netw 2023:5476836. https://doi.org/10.1155/2023/5476836

  24. Dou Z, Zhou X, Yang Q, Yang L, Tian J (2022) Improvement and performance evaluation of IEEE 802.11 p protocol in dense scenario of VANET. Mob Inf Syst 2022:1955948. https://doi.org/10.1155/2022/1955948

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shilpa Bagade.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

List of abbreviations

Table

Table 2 Symbols and notations

2

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bagade, S., Kumar, B.A. & Rao, L.K. Efficient data transmission over 5G Networks with improved accuracy using 802.11p. Multimed Tools Appl 83, 40377–40392 (2024). https://doi.org/10.1007/s11042-023-17156-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17156-1

Keywords

Navigation