International Journal Of Engineering Technology And Management Sciences

IMPLEMENTATION OF UNMANNED AERIAL VEHICLES AS FLYING BASE STATIONS TO ASSIST 5G NETWORKS

AUTHOR(S)

KAKAULA RAMESHWARAMMA, Dr. N MAGESWARI

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.024

ABSTRACT
Current wireless communication networks are not able to accommodate the increase in broadband data and are currently encountering fundamental challenges like higher data rate and Quality of Service (QoS) requirements, energy efficiency and excellent end-to-end performance and user coverage in overcrowded areas and hotspots whilst maintaining extremely low latency and high bandwidth. The deployment of 5G networks aims to address such challenges by introducing multiple advancements to the network and implementing new technologies to evolve new radio networks. This will primarily be done by introducing the 5G New Radio, which is the radio technology that is being developed to support the 5G technologies that will solve the problems mentioned previously. With the New Radio implementation, the next generation networks will accommodate the growing data rates. The networks are expected to attain a mobile data volume per unit area that is 1,000 times higher than current networks. Over 10-100 times the number of current connected devices is expected to be accommodated by 5G networks. Coverage is primarily the crucial problem with 5G networks, requiring the densification of urban areas with heterogeneous networks and the deployment of more closely packed terrestrial MBSs. However, this is not cost-effective and can be more complex as terrestrial network replanning will be required. The issue can be overcome by integrating UAVs into the network infrastructure as FBSs.

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References:

1. Zhenwei Li *, Mengli Jia, “Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network”, Micromachines 2021, 12, 1478. https://doi.org/10.3390/ mi12121478.
2. Wang, C.; Zhao, Z.; Ren, Q.; Xu, Y.; Yu, Y. Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation. Entropy 2019, 21, 168.
3. Chen, Y. A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation. arXiv 2017,arXiv:1704.07502.
4. Strisciuglio, N.; Azzopardi, G.; Vento, M.; Petkov, N. Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach. Vis. Appl. 2016, 27, 1137–1149.
5. Guo, C.; Szemenyei, M.; Pei, Y.; Yi, Y.; Zhou, W. SD-U-net: A Structured Dropout U-Net for Retinal Vessel Segmentation. In Proceedings of the IEEE 19th International Conference on Bioinformatics and Bioengineering, Athens, Greece, 28–30 October2019; pp. 439–444.
6. Alom, M.Z.; Yakopcic, C.; Hasan, M.; Taha, T.M.; Asari, V.K. Recurrent residual U-Net for medical image segmentation. J. Med.Imaging 2019, 6, 014006.
7. Wang, X.; Jiang, X. Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework. Signal Process. 2019, 165, 104–114.
8. Yan, Z.; Yang, X.; Cheng, K.T. A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation. IEEE J. Biomed. Health Inform. 2019, 23, 1427–1436.
9. Soomro, T.A.; Afifi, A.J.; Zheng, L.; Soomro, S.; Gao, J.; Hellwich, O.; Paul, M. Deep Learning Models for Retinal Blood Vessels Segmentation: A Review. IEEE Access 2019, 7, 71696–71717.
10. Suryani, E.; Susilo, M. The hybrid method of SOM artificial neural network and median thresholding for segmentation of blood vessels in the retina image fundus. Int. J. Fuzzy Log. Intell. Syst. 2019, 19, 323–331.

How to Cite This Article:
KAKAULA RAMESHWARAMMA, Dr. N MAGESWARI. IMPLEMENTATION OF UNMANNED AERIAL VEHICLES AS FLYING BASE STATIONS TO ASSIST 5G NETWORKS. ijetms;7(1):168-181. DOI: 10.46647/ijetms.2023.v07i01.024