Adaptive Strategies and Handover Optimization in 5G Networks to Address High Mobility Challenges

This study develops an adaptive handover strategy in 5G networks to tackle high mobility challenges and improve service continuity and quality. The research method uses a 5G network emulator with a topology of four base stations (gNBs) to test user equipment (UE) speeds of 20 km/h and 130 km/h. The adaptive algorithm developed utilizes data from the Received Signal Strength Indicator (RSSI) as well as the speed and direction of user mobility to make optimal handover decisions. The results show that at a speed of 130 km/h, the average handover latency reaches 12 ms, compared to 8 ms at 80 km/h. Additionally, the signal quality (RSSI) is lower at higher speeds, negatively affecting service quality. This adaptive algorithm successfully reduces handover latency and improves the stability and efficiency of the overall network. The testing also indicates that this adaptive algorithm can predict handover targets by considering UE speed and direction and uses hysteresis mechanisms to prevent overly frequent handovers. By employing technologies such as Multiple Input Multiple Output (MIMO) and beamforming, this strategy is expected to maintain high service quality even under high mobility conditions.

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Publication Details

Published
2024-09-12
DOI
https://doi.org/10.1109/iceltics62730.2024.10776365
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
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article

Adaptive Strategies and Handover Optimization in 5G Networks to Address High Mobility Challenges

Murhaban Murhaban, Fitri Arnia, Syahrial Syahrial, Teuku Yuliar Arif
Advanced MIMO Systems Optimization
article

Adaptive Strategies and Handover Optimization in 5G Networks to Address High Mobility Challenges

Murhaban Murhaban, Fitri Arnia, Syahrial Syahrial, Teuku Yuliar Arif
article en

Abstract

This study develops an adaptive handover strategy in 5G networks to tackle high mobility challenges and improve service continuity and quality. The research method uses a 5G network emulator with a topology of four base stations (gNBs) to test user equipment (UE) speeds of 20 km/h and 130 km/h. The adaptive algorithm developed utilizes data from the Received Signal Strength Indicator (RSSI) as well as the speed and direction of user mobility to make optimal handover decisions. The results show that at a speed of 130 km/h, the average handover latency reaches 12 ms, compared to 8 ms at 80 km/h. Additionally, the signal quality (RSSI) is lower at higher speeds, negatively affecting service quality. This adaptive algorithm successfully reduces handover latency and improves the stability and efficiency of the overall network. The testing also indicates that this adaptive algorithm can predict handover targets by considering UE speed and direction and uses hysteresis mechanisms to prevent overly frequent handovers. By employing technologies such as Multiple Input Multiple Output (MIMO) and beamforming, this strategy is expected to maintain high service quality even under high mobility conditions.

Universitas Syiah Kuala (ID), Universitas Teuku Umar (ID)
Openalex Percentile: Top 77%
Advanced MIMO Systems Optimization
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