Published March 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Gibbs Haversine Reinforcement Learning Based Handover For 5g Enabled Seamless Mobility in Wireless Network

Creators

  • 1. Research Scholar, Periyar University, Salem

Contributors

Contact person:

  • 1. Research Scholar, Periyar University, Salem
  • 2. Professor, Periyar University, Salem

Description

Abstract: Seamless Mobility (SM) is crucial for bringing about better Quality of Service (QoS) like minimum handover latency with maximum throughput in 5G networks. In this work a method called, Jenkin Impulse Response Filtering and Reinforcement Learning-based Gibbs Haversine Distribution (JIRF-RLGHD) for optimal selection of target cells for the handover process to ensure seamless mobility is designed. The JIRF-RLGHD method is split into two sections. They are predicting the signal quality of both serving and adjacent wireless nodes using the Box Jenkin Impulse Response Filtering model. The second task remains in applying Reinforcement Learning-based Gibbs Haversine Distribution for optimal selection of target cells for handover to ensure seamless mobility in a wireless network. The overall proposed method was simulated on a Python programming interface language. The simulation results reveal that the JIRF-RLGHD method offers a greater delivery rate, and handover success with lesser handover latency at minimal packet loss rate. Numerical results show that the JIRF-RLGHD method performs better in terms of data delivery rate by 18%, and handover latency by 33% compared to existing methods.

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Additional details

Identifiers

DOI
10.35940/ijrte.F8018.12060324
EISSN
2277-3878

Dates

Accepted
2024-03-15
Manuscript received on 25 February 2024 | Revised Manuscript received on 13 March 2024 | Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024.

References

  • Wasan Kadhim Saad, Ibraheem Shayea, Abdulraqeb Alhammadi, Muntasir Mohammad Sheikh, Ayman A. El-Saleh, "Handover and load balancing self-optimization models in 5G mobile networks", Engineering Science and Technology, an International Journal, Elsevier, Apr 2023 [optimization based on the distance (Opt. Distance)]
  • Raja Karmakar, Georges Kaddoum, Samiran Chattopadhyay, "Mobility Management in 5G and Beyond: A Novel Smart Handover with Adaptive Time-to-Trigger and Hysteresis Margin", IEEE Transactions on Mobile Computing, Oct 2023 [Learning-based Intelligent Mobility Management (LIM2)] https://doi.org/10.1109/TMC.2022.3188212
  • Metin Ozturka, Mandar Gogate, Oluwakayode Onireti, Ahsan Adeel, Amir Hussain, Muhammad A. Imran, "A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)", Neurocomputing, Elsevier, Jan 2019 https://doi.org/10.1016/j.neucom.2019.01.031
  • Waheeb Tashan, Ibraheem Shayea, Sultan Aldirmaz-Çolak, Omar Abdul Aziz, Abdulraqeb Alhammadi, Yousef Ibrahim Daradkeh, "Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions", IEEE Vehicular Technology Society Section, Oct 2022 https://doi.org/10.1109/ACCESS.2022.3215684
  • Lalita Mishra, Vikash, Shirshu Varma, "Seamless Health Monitoring Using 5G NR for Internet of Medical Things", Wireless Personal Communications, Springer, Jul 2021 https://doi.org/10.1007/s11277- 021-08730-7
  • Nadine Akkari, Nikos Dimitriou, "Mobility Management Solutions for 5g Networks: Architecture and Services", Computer Networks, Elsevier, Feb 2020 https://doi.org/10.1016/j.comnet.2019.107082
  • X. Dinga, Q. Qib, S. Jian, "Mechanism design for Mobility-as-a-Service platform considering travelers' strategic behavior and multidimensional requirements",Transportation Research Part B: Methodological, Elsevier, Jul 2023 https://doi.org/10.1016/j.trb.2023.04.004
  • Mohsen Attaran, "The impact of 5G on the evolution of intelligent automation and industry digitization", Journal of Ambient Intelligence and Humanized Computing, Springer, Feb 2021 https://doi.org/10.1007/s12652-020-02521-x
  • Bhanu Priya, Jyoteesh Malhotra, "5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network", Journal of Ambient Intelligence and Humanized Computing, Springer, Feb 2023
  • Venkatraman Balasubramanian, Faisal Zaman, Moayad Aloqaily, Ismaeel Al Ridhawi, Yaser Jararweh, and Haythem Bany Salameh, "A Mobility Management Architecture for Seamless Delivery of 5G-IoT Services", IEEE Xplore, Aug 2019 https://doi.org/10.1109/ICC.2019.8761658
  • Ali Saeed Dayem Alfoudi, S. H. Shah Newaz, Rudy Ramlie, Gyu Myoung Lee, Thar Baker, "Seamless Mobility Management in Heterogeneous 5G Networks: A Coordination Approach among Distributed SDN Controllers", IEEE Vehicular Technology, Aug 2019
  • Ji-Hwan Choi and Dong-Joon Shin, "Generalized RACH-less Handover for Seamless Mobility in 5G and Beyond Mobile Networks", IEEE Wireless Communications Letters, Aug 2019
  • Flavio Morselli, Sara Modarres Razavi, Moe Z. Win, Andrea Conti, "Soft Information Based Localization for 5G Networks and Beyond", IEEE Transactions on Wireless Communications, Mar 2023 https://doi.org/10.1109/TWC.2023.3275122
  • Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah, "A Survey on Handover Optimization in Beyond 5G Mobile Networks: Challenges and Solutions", IEEE Access, Jun 2023 https://doi.org/10.1109/ACCESS.2023.3284905
  • Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan, Kashif Nisar, "Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey", IEEE Access, Jan 2023
  • Amiraslan Haghrah, Mehran Pourmohammad Abdollahi, Hosein Azarhava and Javad Musevi Niya, "A survey on the handover management in 5G-NR cellular networks: aspects, approaches and challenges", EURASIP Journal on Wireless Communications and Networking, Springer, Feb 2023 https://doi.org/10.1186/s13638-023- 02261-4
  • Amal Chaffai, "Mobility Management in 5G Network on the Efficiency Hierarchical State of Slicing", Springer, Oct 2021 https://doi.org/10.21203/rs.3.rs-2135964/v1
  • A. Saraswathi Priyadharshini P.T.V. Bhuvaneswari, "Regression model for seamless mobility in LTE-A HetNets", Wiley, Jan 2018 https://doi.org/10.1002/ett.3570
  • Mubarak S. Almutairi, "Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction", Mathematical Problems in Engineering, Hindawi, Jan 2022 https://doi.org/10.1155/2022/6855435
  • Zihang Gao, "Research on 5G Network Slicing Strategy for Urban Complex Environment", Wireless Communications and Mobile Computing, Wiley, Jun 2023 https://doi.org/10.1155/2023/2820966
  • Al – Amodi, A. M. A., & Dattaisa, Dr. A. (2020). The Impact of Heterogenous Ultra-dense Network Technologies on the Performance of 4G and 5GNetworks. In International Journal of Innovative Technology and Exploring Engineering (Vol. 10, Issue 1, pp. 35–44). https://doi.org/10.35940/ijitee.a8070.1110120
  • Neuro-Fuzzy based Call Admission Control for next Generation Mobile Multimedia Networks. (2019). In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 2057–2066). https://doi.org/10.35940/ijeat.f8470.088619
  • Anandhavel, B., & Prabakaran, Dr. T. E. (2019). A Learn Of Fuzzy Regression Model and Its Applications. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 2, pp. 2967– 2971). https://doi.org/10.35940/ijrte.b2415.078219
  • Singh, H., & Marwaha, C. (2019). Fog Computing: Terminologies, Architecture and Challenges. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 3, pp. 7–13). https://doi.org/10.35940/ijitee.c1162.116319
  • Radhamani, V., & Dalin, G. (2019). Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review. In International Journal of Soft Computing and Engineering (Vol. 9, Issue 3, pp. 1–7). https://doi.org/10.35940/ijsce.c3265.099319
  • Sharma, P., & Site, S. (2022). A Comprehensive Study on Different Machine Learning Techniques to Predict Heart Disease. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 2, Issue 3, pp. 1–7). https://doi.org/10.54105/ijainn.c1046.042322