Mobility-Cartesian Handover Scheme: A Novel Approach for Seamless Communication for User Equipment in MillimeterWave Heterogeneous Networks

Frankin Chibueze Njoku, Zakka Augustine, Umar-Faruk Abdu-Aguye, Salome Danjuma Mammmanjabs, Philimon Bala Sambo

Abstract


The Mobility-Cartesian handover (MCH) scheme is an advanced communication technique designed to ensure seamless connectivity for user equipment (UE) during handovers in mobile networks. This approach is based on the Cartesian coordinate system, which facilitates efficient and accurate positioning of UEs and enables faster handover decisions. In traditional handover schemes, UEs new serving base station (BS) must be identified and synchronized with the old BS before the handover can be completed. This process can lead to significant delay and potential loss of connectivity, especially during high-speed mobility or in dense network environments. The mobility-Cartesian handover scheme aims to address these challenges by modeling the user mobility patterns as obstacle-based and obstacle-free coefficients in relation to the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) communication links whilst computing the handover exponents with respect to the handover margin and Signal-to-interference plus noise ratio (SINR). The performance metric of average system throughput is used to demonstrate the superiority of the proposed model when compared with the standard Rate-based handover (RBH) and the Double Deep Reinforcement Learning handover (DDRL) schemes. Simulation results showed that the developed scheme in terms of mmWave BS density, obtained an average throughput of 35.25% and 2.68% improvement, respectively when compared to the RBH and the DDRL schemes. Furthermore, the developed scheme in terms of user velocity, obtained 23.06% and 2.20% average throughput percentage improvement, respectively when compared to the RBH and DDRL schemes.


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References


GPP TR 38.821. (2019). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Solutions for NR to Support Non-Terrestrial Networks (NTN) (Release 15).

GPP, (2013) "Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall Description; Stage 2," Technical Report, 3GPP TS 36.300 Version 11.5.0, Release 11.

GPP, (2014) "Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall Description; Stage 2," Technical Report, 3GPP TS 36.300 Version 12.5.0, Release 12.

Al Achhab, T., Abboud, F., & Assalem, A. (2021). A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks. IEEE Access, 9, 154237-154264.

Crutchfield, J. P., & McNamara, B. S. (1987). Equations of Motion from a Data Series Complex system, 1, 417-452

Deswal, S., & Singhrova, A. (2017). A Vertical Handover Algorithm in Integrated Macrocell Femtocell Networks. International Journal of Electrical & Computer Engineering (2088-8708), 7(1).

Dzaferagic, M., Marchetti, N., & Macaluso, I. (2020). Minimizing the Signaling Overhead and Latency Based on Users’ Mobility Patterns. IEEE Systems Journal, 15(1), 77-84.

Koda, Y., Yamamoto, K., Nishio, T., & Morikura, M. (2018, April). Reinforcement learning based predictive handover for pedestrian-aware mmWave networks. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 692-697). IEEE.

Koda, Y., Nakashima, K., Yamamoto, K., Nishio, T., & Morikura, M. (2019). Handover management for mmWave networks with proactive performance prediction using camera images and deep reinforcement learning. IEEE Transactions on Cognitive Communications and Networking, 6(2), 802-816.

Mollel, M., Ozturk, M., Kisangiri, M., Kaijage, S., Onireti, O., Imran, M. A., & Abbasi, Q. H. (2019). Handover management in dense networks with coverage prediction from sparse networks. In 2019 IEEE Wireless Communications and Networking Conference Workshop 17(9), 1-6.

Mollel, M. S., Abubakar, A. I., Ozturk, M., Kaijage, S., Kisangiri, M., Zoha, A., ... & Abbasi, Q. H. (2020). Intelligent handover decision scheme using double deep reinforcement learning. Physical Communication, 42, 101133.

Ngo, H. Q. (2015). Massive MIMO: Fundamentals and system designs (Vol. 1642). Linköping University Electronic Press.

Polese, M., Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2017). Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 2069-2084.

Priyanka, A., & Chandrasekhar, C. (2023). Proactive Decision Making for Handover Management on Heterogeneous Networks. In International Conference on Information, Communication and Computing Technology (pp. 629-648). Singapore: Springer Nature Singapore.

Qi, K., Liu, T., & Yang, C. (2021). Federated learning based proactive handover in millimeter-wave vehicular networks. In 2021 15th IEEE International Conference on Signal Processing (ICSP) 42(1), 401-406.

Shayea, I., Ergen, M., Azizan, A., Ismail, M., & Daradkeh, Y. I. (2020). Individualistic dynamic handover parameter self-optimization algorithm for 5G networks based on automatic weight function. IEEE Access, 8, 214392-214412.

Silva, K. D. C., Becvar, Z., & Frances, C. R. L. (2018). Adaptive hysteresis margin based on fuzzy logic for handover in mobile networks with dense small cells. IEEE Access, 6, 17178-17189.

Sarkar, S., & Ghosh, S. C. (2023). Mobility Aware Path Selection for Millimeterwave 5G Networks in the Presence of Obstacles. In International Conference on Computer and Communication Engineering (pp. 67-80). 11(3), 67-80.

Wang, C. X., Haider, F., Gao, X., You, X. H., Yang, Y., Yuan, D. & Hepsaydir, E. (2014). Cellular architecture and key technologies for 5G wireless communication networks. IEEE communications magazine, 52(2), 122-130.

Wang, H., & Li, B. (2024). Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity. Transactions on Emerging Telecommunications Technologies, 35(1), 4907.

Wu, X., & O’Brien, D. C. (2020). A novel machine learning-based handover scheme for hybrid LiFi and WiFi networks. In 2020 IEEE Globecom Workshops (GC Wkshps 10(3), 1-5.

Yu, J., Zhang, X., & Chen, Y. (2021). Joint optimization of access delay and handover latency in 5G satellite-terrestrial integrated networks. IEEE Transactions on Vehicular Technology, 70(1), 464-476.

Zhang, Z., Jiang, Z., Yang, B., & She, X. (2023). A Beamforming-Based Enhanced Handover Scheme with Adaptive Threshold for 5G Heterogeneous Networks. Electronics, 12(19), 4131.

Zhou, H., Zhou, H., Li, J., Yang, K., An, J., & Shen, X. (2023). Heterogeneous ultra-dense networks with traffic hotspots: A unified handover analysis. IEEE Internet of Things Journal.

Zhu, H., & Peng, Y. I. (2020). Research on adaptive handover scheme based on improved genetic algorithm. Procedia Computer Science, 166, 557-562.


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