International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Investigating Resource Allocation Techniques and Key Performance Indicators (KPIs) for 5G New Radio Networks: A Review

Author NameAuthor Details

Jyoti, Amandeep, Dharmender Kumar

Jyoti[1]

Amandeep[2]

Dharmender Kumar[3]

[1]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

[2]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

[3]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

Cite this Article

DOI: 10.22247/ijcna/2023/221899

Pages : 422-442

 Download PDF

1385

Views

Abstract

The demand for 5G networks is growing day by day, but there remain issues regarding resource allocation. Moreover, there is a need to focus on key performance indicators for the 5G network. This study looks at the assessment of 5G wireless communications as well as the minimal technical performance criteria for 5G network services according to the ITU-R, Next Generation Mobile, 3GPP, and Networks. 5G standards that have been created in the 3GPP, ITU-Telecommunication Standardization Sector, ITU-R Sector, Internet Engineering Task Force, and IEEE are covered. In 5G-based wireless communication systems, resource allocation is a key activity that must be done. It is essential for the new systems used in 5G wireless networks to be more dynamic and intelligent if they are going to be able to satisfy a range of network requirements at the same time. This may be accomplished via the use of new wireless technologies and methods. Key characteristics of 5G, such as waveform, dynamic slot-based frame structure, massive MIMO, and channel codecs, have been explained, along with emerging technologies in the 5G network. Previous research related to 5G networks that considered resource allocation in heterogeneous networks is elaborated, along with the requirement of KPIs for 5G networks. The functionality of 5G has been discussed, along with its common and technological challenges. The research paper has also focused on metrics, indicators, and parameters during resource allocation in 5G, along with machine learning. To move the massive amounts of data that may flow at speeds of up to 100 Gbps/km2, these devices need supplementary, well-organized, and widely deployed RATs. To accommodate the expected exponential growth in the data flow, 5G network RAN radio blocking and resource management solutions would need to be able to handle more than 1,000 times the present traffic level. In addition, all of the information that makes up this traffic must be available and shareable at any time, from any location, and using any device inside the 5G RAN and beyond 4G cellular coverage areas. The need for resource allocation is discussed, along with the existing algorithm and improvements made in technology for resource allocation.

Index Terms

5G Networks

5G Services

Resource Allocation

5G Technologies

5G KPIs

ITU-R

Reference

  1. 1.
    M. Z. Asghar, S. A. Memon, and J. Hamalainen, “Evolution of Wireless Communication to 6G: Potential Applications and Research Directions,” Sustainability, vol. 14, no. 10, p. 6356, May 2022, doi: 10.3390/su14106356.
  2. 2.
    H. Yu, H. Lee, and H. Jeon, “What is 5G? Emerging 5G Mobile Services and Network Requirements,” Sustainability, vol. 9, no. 10, p. 1848, Oct. 2017, doi: 10.3390/su9101848.
  3. 3.
    R. Beaubrun, “Technical Challenges and Categorization of 5G Mobile Services,” in 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain, Jul. 2022, pp. 345–350. doi: 10.1109/ICUFN55119.2022.9829623.
  4. 4.
    I. F. Akyildiz, S. Nie, S.-C. Lin, and M. Chandrasekaran, “5G roadmap: 10 key enabling technologies,” Computer Networks, vol. 106, pp. 17–48, Sep. 2016, doi: 10.1016/j.comnet.2016.06.010.
  5. 5.
    C. Sudhamani, M. Roslee, J. J. Tiang, and A. U. Rehman, “A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges,” Sensors, vol. 23, no. 4, p. 2356, Feb. 2023, doi: 10.3390/s23042356.
  6. 6.
    “IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond”.
  7. 7.
    S. Henry, A. Alsohaily, and E. S. Sousa, "5G are Real: Evaluating the Compliance of the 3GPP 5G New Radio System with the ITU IMT-2020 Requirements," IEEE Access, vol. 8, no. 5, pp. 42828–42840, 2020, doi: 10.1109/ACCESS.2020.2977406.
  8. 8.
    https://www.qualcomm.com/news/onq/2017/08/understanding-3gpp-starting-basics (Accessed on 20/March/2023)
  9. 9.
    A. Ghosh, A. Maeder, M. Baker, and D. Chandramouli, “5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 15,” IEEE Access, vol. 7, pp. 127639–127651, 2019, doi: 10.1109/ACCESS.2019.2939938.
  10. 10.
    G. Liu, Y. Huang, Z. Chen, L. Liu, Q. Wang, and N. Li, “5G Deployment: Standalone vs. Non-Standalone from the Operator Perspective,” IEEE Commun. Mag., vol. 58, no. 11, pp. 83–89, Nov. 2020, doi: 10.1109/MCOM.001.2000230.
  11. 11.
    https://assets.rbl.ms/25586498/origin.png (Accessed on 20/March/2023)
  12. 12.
    https://stlpartners.com/wp-content/uploads/2022/03/5G-standalone-vs.-5G-non-standalone-crop.jpeg (Accessed on 21/ March/2023)
  13. 13.
    https://images.samsung.com/is/content/samsung/p5/global/business/networks/insights/event/the-silicon-valley-5g-summit-2017/Session-1_3GPP_Balazs-Bertenyi.pdf. (Accessed on 1/April/2023)
  14. 14.
    Y. Han, S. E. Elayoubi, A. Galindo-Serrano, V. S. Varma, and M. Messai, “Periodic Radio Resource Allocation to Meet Latency and Reliability Requirements in 5G Networks,” in 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto: IEEE, Jun. 2018, pp. 1–6. doi: 10.1109/VTCSpring.2018.8417636.
  15. 15.
    M. Agarwal, A. Roy, and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016, doi: 10.1109/COMST.2016.2532458.
  16. 16.
    S. Parkvall, E. Dahlman, A. Furuskar, and M. Frenne, “NR: The New 5G Radio Access Technology,” IEEE Comm. Stand. Mag., vol. 1, no. 4, pp. 24–30, Dec. 2017, doi: 10.1109/MCOMSTD.2017.1700042.
  17. 17.
    N. Sharma and K. Kumar, “Resource allocation trends for ultra-dense networks in 5G and beyond networks: A classification and comprehensive survey,” Physical Communication, vol. 48, p. 101415, Oct. 2021, doi: 10.1016/j.phycom.2021.101415.
  18. 18.
    V. Kumar, S. Yadav, D. N. Sandeep, S. B. Dhok, R. K. Barik, and H. Dubey, “5G Cellular: Concept, Research Work and Enabling Technologies,” in Advances in Data and Information Sciences, vol. 39, M. L. Kolhe, M. C. Trivedi, S. Tiwari, and V. K. Singh, Eds. Singapore: Springer Singapore, 2019, pp. 327–338. doi 10.1007/978-981-13-0277-0_27.
  19. 19.
    K. Sakaguchi, G.K.Tran., "Millimeter-wave Evolution for 5G Cellular Networks,” IEICE Trans. Commun., vol. E98.B, no. 3, pp. 388–402, 2015, doi: 10.1587/Transcom.E98.B.388.
  20. 20.
    A. Imran and A. Zoha, “Challenges in 5G: how to empower SON with big data for enabling 5G,” IEEENetwork, vol. 28, no. 6, pp. 27–33, Nov. 2014, DOI: 10.1109/MNET.2014.6963801.
  21. 21.
    R. Chataut and R. Akl, “Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction,” Sensors, vol. 20, no. 10, p. 2753, May 2020, doi 10.3390/s20102753.
  22. 22.
    M. A. Kamal, H. W. Raza, M. M. Alam, M. M. Su’ud, and A. binti A. B. Sajak, “Resource Allocation Schemes for 5G Network: A Systematic Review,” Sensors, vol. 21, no. 19, p. 6588, Oct. 2021, doi: 10.3390/s21196588.
  23. 23.
    H. Fourati, R. Maaloul, and L. Chaari, A survey of 5G network systems: challenges and machine learning approaches, vol. 12, no. 2. Springer Berlin Heidelberg, 2021.
  24. 24.
    M. Fuentes, “5G New Radio Evaluation Against IMT-2020 Key Performance Indicators,” IEEE Access, vol. 8, pp. 110880–110896, 2020, doi: 10.1109/ACCESS.2020.3001641.
  25. 25.
    A.Benebbour,”IMT-2020 Radio Interface Standardization Trends in ITU-R”. Vol19_3007en pdf. (Accessed on 20/March/2023)
  26. 26.
    T. Norp and Senior Business Consultant at TNO, 3GPP SA1 chairman at KPN, The Netherlands, “5G Requirements and Key Performance Indicators,” JICTS, vol. 6, no. 1, pp. 15–30, 2018, doi: 10.13052/jicts2245-800X.612.
  27. 27.
    K. L. Bhawan, “5G-Key Capabilities & Applications”. https://www.tec.gov.in/pdf/Studypaper/5G%20Study%20Paper-approved%20by%20Sr%20DDG.pdf (Accessed on 20/March/2023)
  28. 28.
    S. A. Abdel Hakeem, H. H. Hussein, and H. Kim, “Vision and research directions of 6G technologies and applications,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 2419–2442, Jun. 2022, doi: 10.1016/j.jksuci.2022.03.019.
  29. 29.
    W. Ejaz, S. K. Sharma, S. Saadat, M. Naeem, A. Anpalagan, and N. A. Chughtai, “A comprehensive survey on resource allocation for CRAN in 5G and beyond networks,” Journal of Network and Computer Applications, vol. 160, pp. 102638, Jun. 2020, DOI: 10.1016/j.jnca.2020.102638.
  30. 30.
    B. G. Gopal and P. G. Kuppusamy, “A Comparative Study on 4G and 5G Technology for Wireless Applications,” IOSR J. Electron. Commun. Eng., vol. 10, no. 6, pp. 2278–2834, 2015, DOI: 10.9790/2834-10636772.
  31. 31.
    S. Moon, B. Kim, S. Malik, C.You, H.Liu, and I.Hwang “Cell Selection and Resource Allocation for Interference Management in a Macro-Picocell Heterogeneous Network,” Wireless PersCommun, vol. 83, no. 3, pp. 1887–1901, Aug. 2015, DOI: 10.1007/s11277-015-2489-9.
  32. 32.
    Y. Xu and G. Li, “Optimal and Robust Interference Efficiency Maximization for Multicell Heterogeneous Networks,” IEEE Access, vol. 7, pp. 102406–102416, 2019, DOI: 10.1109/ACCESS.2019.2931863.
  33. 33.
    A. Pratap, R. Misra, and S. K. Das, “Resource Allocation to Maximize Fairness and Minimize Interference for Maximum Spectrum Reuse in 5G Cellular Networks,” in 2018 IEEE 19th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Chania, Greece, Jun. 2018, pp. 1–9. DOI: 10.1109/WoWMoM.2018.8449760.
  34. 34.
    W. Ejaz, S. K. Sharma, S. Saadat, M. Naeem, A. Anpalagan, and N. A. Chughtai, “A comprehensive survey on resource allocation for CRAN in 5G and beyond networks,” Journal of Network and Computer Applications, vol. 160, p. 102638, Jun. 2020, doi: 10.1016/j.jnca.2020.102638.
  35. 35.
    H. Zhang, M. Feng, K. Long, G. K. Karagiannidis, and V. C. M. Leung, “Energy-Efficient Resource Allocation in NOMA Heterogeneous Networks with Energy Harvesting,” 2018 IEEE Glob. Commun.Conf.GLOBECOM 2018 - Proc., no. April, pp. 48–53, 2018, DOI: 10.1109/GLOCOM.2018.8647140.
  36. 36.
    M. Ghanbarisabagh, G. Vetharatnam, E. Giacoumidis, and S. MomeniMalayer, “Capacity Improvement in 5G Networks Using Femtocell,” Wireless PersCommun, vol. 105, no. 3, pp. 1027–1038, Apr. 2019, DOI: 10.1007/s11277-019-06134-2.
  37. 37.
    J. Ghosh, “Interrelationship between Energy Ef?ciency and Spectral Ef?ciency in Cognitive Femtocell Networks: A Survey”. https://www.researchgate.net/publication/349136615_Interrelationship_between_Energy_Efficiency_and_Spectral_Efficiency_in_Cognitive_Femtocell_Networks_A_Survey/link/6413c16892cfd54f8407777b/download. (Accessed on 20/March/2023)
  38. 38.
    N. Jinaporn, S. Armour, and A. Doufexi, “Performance Evaluation on Resource Allocation with Carrier Aggregation in LTE Cellular Networks,” in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA, Sep. 2019, pp. 1–5. DOI: 10.1109/VTCFall.2019.8891246.
  39. 39.
    A. Pratap, R. Misra, and S. K. Das, “Maximizing Fairness for Resource Allocation in Heterogeneous 5G Networks,” IEEE Trans. on Mobile Comput., vol. 20, pp. 603–619, Feb. 2021, DOI: 10.1109/TMC.2019.2948877.
  40. 40.
    Y. Xu, G. Gui, H. Gacanin, and F. Adachi, “A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges,” IEEE Commun. Surv. Tutorials, vol. 23, pp. 668–695, 2021, DOI: 10.1109/COMST.2021.3059896.
  41. 41.
    D. S. Kumar, and A. Noliya, “A Comprehensive Review on Resource Allocation Techniques in LTE- Advanced Small Cell Heterogeneous Networks,” Control Systems, vol. 10, pp.15, 2018.
  42. 42.
    V.Chauhan and Amandeep, “A Review on Resource Allocation in Heterogeneous LTE-Advanced Networks,” in International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, vol. 26, J. Hemanth, X. Fernando, P. Lafata, and Z. Baig, Eds. Cham: Springer International Publishing, 2019, pp. 1122–1127. DOI: 10.1007/978-3-030-03146-6_130.
  43. 43.
    A. Noliya and S. Kumar, “Performance Analysis of Resource Scheduling Techniques in Homogeneous and Heterogeneous Small Cell LTE-A Networks,” Wireless PersCommun, vol. 112, no. 4, pp. 2393–2422, Jun. 2020, DOI:10.1007/s11277-020-07156-x.
  44. 44.
    C. Niu, Y. Li, R. Qingyang Hu, and F. Yet, “Femtocell-enhanced multi-target spectrum allocation strategy in LTE-A HetNets,” IET Communications, vol. 11, no. 6, pp. 887–896, Apr. 2017, DOI: 10.1049/get-com.2016.1256.
  45. 45.
    F. Fang, J. Cheng, and Z. Ding, “Joint Energy Efficient Subchannel and Power Optimization for a Downlink NOMA Heterogeneous Network”, IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1351–1364, Feb. 2019.
  46. 46.
    A. Imran and A. Zoha, “Challenges in 5G: how to empower SON with big data for enabling 5G,” IEEENetwork, vol. 28, no. 6, pp. 27–33, Nov. 2014, DOI: 10.1109/MNET.2014.6963801.
  47. 47.
    M.U. Iqbal, “Machine learning based capacity enhancement of femtocells in case of 5G heterogeneous networks,” vol. 71, pp. 2411, 2019.
  48. 48.
    M. E. Morocho-Cayamcela, H. Lee, and W. Lim, “Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions,” IEEE Access, vol. 7, pp. 137184–137206, 2019, DOI: 10.1109/ACCESS.2019.2942390.
  49. 49.
    R. Li, “Intelligent 5G: When Cellular Networks Meet Artificial Intelligence,” IEEE Wireless Commun., vol. 24, no. 5, pp. 175–183, Oct. 2017, DOI: 10.1109/MWC.2017.1600304WC
  50. 50.
    D. Liu, L. Wang, Y. Chen, K. Wong, and L. Hanzo, “User Association in 5G Networks: A Survey and an Outlook,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 1018–1044, 2016, DOI: 10.1109/COMST.2016.2516538.
  51. 51.
    S. Parkvall, E. Dahlman, A. Furuskar, and M. Frenne, “NR: The New 5G Radio” DOI: 10.1109/MCOMSTD.2017.1700042.
  52. 52.
    M. Humayun, B. Hamid, N. Jhanjhi, G. Suseendran, and M. N. Talib, “5G Network Security Issues, Challenges, Opportunities and Future Directions: A Survey,” J. Phys.: Conf. Ser., vol. 1979, no. 1, p. 012037, Aug. 2021, doi: 10.1088/1742-6596/1979/1/012037.
  53. 53.
    W. Chin, Z. Fan, and R. Haines, “Emerging technologies and research challenges for 5G wireless networks,” IEEE Wireless Commun., vol. 21, no. 2, pp. 106–112, Apr. 2014, doi: 10.1109/MWC.2014.6812298.
  54. 54.
    H. F. Alhashimi et al., “A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges,” Electronics, vol. 12, no. 3, p. 647, Jan. 2023, doi 10.3390/electronics12030647.
  55. 55.
    Z. Mohammadian, M. J. Dehghani, and M. Eslami, “Efficient resource allocation algorithms for high energy efficiency with fairness among users in OFDMA networks,” Engineering Science and Technology, an International Journal, vol. 23, no. 5, pp. 982–988, Oct. 2020, doi: 10.1016/j.jestch.2020.01.003.
  56. 56.
    N. Bonjorn, F. Foukalas, F. Canellas, and P. Pop, “Cooperative Resource Allocation and Scheduling for 5G eV2X Services,” IEEE Access, vol. 7, pp. 58212–58220, 2019, doi: 10.1109/ACCESS.2018.2889190.
  57. 57.
    A. K. Bashir, R. Arul, S. Basheer, G. Raja, R. Jayaraman, and N. M. F. Qureshi, “An optimal multitier resource allocation of cloud RAN in 5G using machine learning,” Trans Emerging Tel Tech, vol. 30, no. 8, Aug. 2019, doi: 10.1002/ett.3627.
  58. 58.
    I. AlQerm and B. Shihada, “Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks,” IEEE Trans. on Mobile Computer., vol. 17, no. 10, pp. 2423–2437, Oct. 2018, doi: 10.1109/TMC.2018.2797166.
  59. 59.
    I. AlQerm and B. Shihada, “A cooperative online learning scheme for resource allocation in 5G systems,” in 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016, pp. 1–7. doi: 10.1109/ICC.2016.7511617.
  60. 60.
    B. Xu, Y. Chen, J. R. Carrion, and T. Zhang, “Resource Allocation in Energy-Cooperation Enabled Two-Tier NOMA HetNetsToward Green 5G,” IEEE J. Select. Areas Communication. vol. 35, no. 12, pp. 2758–2770, Dec. 2017, doi: 10.1109/JSAC.2017.2726398.
  61. 61.
    S. Imtiaz, H. Ghauch, G. P. Koudouridis, and J. Gross, “Random forests resource allocation for 5G systems: Performance and robustness study,” in 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Barcelona, Apr. 2018, pp. 326–331. doi 10.1109/WCNCW.2018.8369028.
  62. 62.
    J. Yun, Md. J. Piran, and D. Y. Suh, “QoE-Driven Resource Allocation for Live Video Streaming Over D2D-Underlaid 5G Cellular Networks,” IEEE Access, vol. 6, pp. 72563–72580, 2018, doi: 10.1109/ACCESS.2018.2882441.
  63. 63.
    X. Song, K. Wang, L. Lei, L. Zhao, Y. Li, and J. Wang, “Interference Minimization Resource Allocation for V2X Communication Underlying 5G Cellular Networks,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–9, Sep. 2020, doi: 10.1155/2020/2985367.
  64. 64.
    Y. Han, S. E. Elayoubi, A. Galindo-Serrano, V. S. Varma, and M. Messai, “Periodic Radio Resource Allocation to Meet Latency and Reliability Requirements in 5G Networks,” in 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, Jun. 2018, pp. 1–6. doi 10.1109/VTCSpring.2018.8417636.
  65. 65.
    M. Agarwal, A. Roy, and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey,” IEEE Communication. Surv. Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016, doi: 10.1109/COMST.2016.2532458.
  66. 66.
    S. Imtiaz, H. Ghauch, M. M. U. Rahman, G. Koudouridis, and J. Gross, "Learning-Based Resource Allocation Scheme for TDD-Based 5G CRAN System," in Proceedings of the 19th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, Malta Malta, Nov. 2016, pp. 176–185. doi 10.1145/2988287.2989158.
  67. 67.
    M. Moltafet, R. Joda, N. Mokari, M. R. Sabagh, and M. Zorzi, “Joint Access and Fronthaul Radio Resource Allocation in PD-NOMA-Based 5G Networks Enabling Dual Connectivity and CoMP,” IEEE Trans. Communication., vol. 66, no. 12, pp. 6463–6477, Dec. 2018, doi: 10.1109/TCOMM.2018.2865766.
  68. 68.
    N. S. Saba Farheen and A. Jain, "Improved routing in MANET with optimized multi-path routing finetuned with hybrid modeling,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 2443–2450, Jun. 2022, doi: 10.1016/j.jksuci.2020.01.001.
  69. 69.
    W. U. Rehman, T. Salam, A. Almogren, K. Haseeb, I. Ud Din, and S. H. Bouk, “Improved Resource Allocation in 5G MTC Networks,” IEEE Access, vol. 8, pp. 49187–49197, 2020, doi: 10.1109/ACCESS.2020.2974632.
  70. 70.
    P. Hao, X. Yan, J. Li, Y.-N.Ruyue Li, and H. Wu, “Flexible Resource Allocation in 5G Ultra Dense Network with Self-Backhaul,” in 2015 IEEE Globecom Workshops (GC Wkshps), San Diego, CA, Dec. 2015, pp. 1–6. doi: 10.1109/GLOCOMW.2015.7414218.
  71. 71.
    G. D. Swetha and G. R. Murthy, “Fair resource allocation for D2D communication in mmwave 5G networks,” in 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Budva, Montenegro, Jun. 2017, pp. 1–6. doi: 10.1109/MedHocNet.2017.8001654.
  72. 72.
    R. P. Mathur, A. Pratap, and R. Misra, “Distributed Algorithm for Resource Allocation in Uplink 5G Networks,” in Proceedings of the 7th ACM International Workshop on Mobility, Interference, and MiddleWare Management in HetNets, Chennai India, Jul. 2017, pp. 1–6. doi: 10.1145/3083201.3083204.
  73. 73.
    P. K. Mishra, S. Pandey, S. K. Udgata, and S. K. Biswash, “Device-centric resource allocation scheme for 5G networks,” Physical Communication, vol. 26, pp. 175–184, Feb. 2018, doi: 10.1016/j.phycom.2017.12.003.
  74. 74.
    F. Tang, Y. Zhou, and N. Kato, “Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet,” IEEE J. Select. Areas Commun., vol. 38, no. 12, pp. 2773–2782, Dec. 2020, doi: 10.1109/JSAC.2020.3005495.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions