Adaptive Handover Management in High-Mobility Networks for Smart Cities
The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/1/23 |
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| author | Yahya S. Junejo Faisal K. Shaikh Bhawani S. Chowdhry Waleed Ejaz |
| author_facet | Yahya S. Junejo Faisal K. Shaikh Bhawani S. Chowdhry Waleed Ejaz |
| author_sort | Yahya S. Junejo |
| collection | DOAJ |
| description | The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios. |
| format | Article |
| id | doaj-art-8ac04b6556494583826f521b1f96e56d |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-8ac04b6556494583826f521b1f96e56d2025-01-24T13:27:54ZengMDPI AGComputers2073-431X2025-01-011412310.3390/computers14010023Adaptive Handover Management in High-Mobility Networks for Smart CitiesYahya S. Junejo0Faisal K. Shaikh1Bhawani S. Chowdhry2Waleed Ejaz3Department of Electrical and Computer Engineering, Lakehead University, Barrie Campus, Barrie, ON L4M 3X9, CanadaDepartment of Telecommunication Engineering, Mehran Univerity of Engineering and Technology, Jamshoro 76062, Sindh, PakistanDepartment of Telecommunication Engineering, Mehran Univerity of Engineering and Technology, Jamshoro 76062, Sindh, PakistanDepartment of Electrical and Computer Engineering, Lakehead University, Barrie Campus, Barrie, ON L4M 3X9, CanadaThe seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios.https://www.mdpi.com/2073-431X/14/1/235G-NRsmart citieshandover success ratetime-to-triggerhigh-speed mobilityadaptive handovers |
| spellingShingle | Yahya S. Junejo Faisal K. Shaikh Bhawani S. Chowdhry Waleed Ejaz Adaptive Handover Management in High-Mobility Networks for Smart Cities Computers 5G-NR smart cities handover success rate time-to-trigger high-speed mobility adaptive handovers |
| title | Adaptive Handover Management in High-Mobility Networks for Smart Cities |
| title_full | Adaptive Handover Management in High-Mobility Networks for Smart Cities |
| title_fullStr | Adaptive Handover Management in High-Mobility Networks for Smart Cities |
| title_full_unstemmed | Adaptive Handover Management in High-Mobility Networks for Smart Cities |
| title_short | Adaptive Handover Management in High-Mobility Networks for Smart Cities |
| title_sort | adaptive handover management in high mobility networks for smart cities |
| topic | 5G-NR smart cities handover success rate time-to-trigger high-speed mobility adaptive handovers |
| url | https://www.mdpi.com/2073-431X/14/1/23 |
| work_keys_str_mv | AT yahyasjunejo adaptivehandovermanagementinhighmobilitynetworksforsmartcities AT faisalkshaikh adaptivehandovermanagementinhighmobilitynetworksforsmartcities AT bhawanischowdhry adaptivehandovermanagementinhighmobilitynetworksforsmartcities AT waleedejaz adaptivehandovermanagementinhighmobilitynetworksforsmartcities |