Handover Techniques in New Generation Wireless Networks

Gupta, Vinodini; Bonde, Padma

doi:10.1007/978-981-10-5544-7_13

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…For instance, ML algorithms can be trained to predict the quality of the new connection before committing to the handover, which can help to minimize the interruption of the call and increase the chances of a successful handover. Many researchers have studied handover technique in new generation wireless networks [14][15][16][17][18][19][20], ranging from traditional techniques such as multi-attribute decision making (MADM) [21] to deep learning techniques such as the SINR change of a UE in the handover problem in 5G networks [22]. In [22][23][24][25][26], the authors combined their efforts to address two issues and proposed a learning-based load balancing handover for multi-user mobile mmWave networks where they characterized the user association as a non-convex optimization problem, and then they attempted to approximate the optimization solution of the problem by using the deep deterministic policy gradient (DDPG) method.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

2023

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The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources mounted on satellites and includes satellite-based communications networks, high altitude platform systems (HAPS), and air-to-ground networks. The fifth generation (5G) and NTN may be crucial in utilizing communication infrastructure to provide 5G services in the future, anytime and anywhere. Based on the outcome of the Rel-16 study, the 3rd generation partnership project (3GPP) decided to start a work item on an NTN in 5G new radio (NR) Rel-17, and the focus of the study was on mobility management procedures, due to the movements of NTN platforms; especially, low earth orbit (LEO) satellites. Handover enhancements were discussed to tackle the frequent handover due to the fast satellite movement. Therefore, the major problem of handover in LEO satellite systems was the signaling storm created by handing over all user equipment (UE) in a cell to a new cell because when the UE crosses the boundary between the neighboring cell of a satellite, an intra-satellite or cell handover occurs; thus, all users in a cell are expected to experience a change of cell due to handover every few seconds. In addition, UE location is not easy to define due to moving cell/beam situations. In this study, we propose machine learning-based solutions for handover decisions in non-terrestrial networks for cell handovers or intra-satellite handovers to reduce signaling storms during handovers where the handover requests will be executed by clustered users. First, the dataset was generated by the simulator that simulates communication between users and satellites. Second, we preprocessed the data, and also used the feature creation technique to create the distance feature using the Haversine formula, and then applied clustering and classification algorithms. The experimental results show that the distance between a user and its cell center is an important parameter for handover decisions in NTN, and the random forest outperforms all models with a higher accuracy of 99% along with a better F1-score of 0.9961.

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