AI-Based Approaches for Handover Optimization in 5G New Radio and 6G Wireless Networks

Ahmed F. Ashour, M. Fouda
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Abstract

In the future, communication networks such as fifth-generation new radio (5G NR) and sixth-generation (6G) will require large data rates and capacities. As a result, mmWave and terahertz (THz) bands are being employed to meet these demands. Unfortunately, these high-frequency bands are susceptible to high path loss, necessitating the deployment of small cells. This, in turn, calls for the installation of a massive number of base stations to cover the whole area. The sheer number of cells and users in such a setup can lead to interruptions in calls when users switch cells, a process known as handover (HO). This has a negative effect on the quality of service (QoS) and the quality of experience (QoE). Therefore, this survey focuses on exploring and comparing artificial intelligence (AI)-based intelligent HO solutions that can optimize HO in 5G NR and 6G networks.
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基于人工智能的5G新无线电和6G无线网络切换优化方法
未来,第五代新无线电(5G NR)和第六代(6G)等通信网络将需要大数据速率和容量。因此,毫米波和太赫兹(THz)频段被用来满足这些需求。不幸的是,这些高频频段容易受到高路径损耗的影响,因此需要部署小型基站。这反过来又要求安装大量的基站以覆盖整个地区。在这种设置中,大量的小区和用户可能导致用户切换小区时呼叫中断,这一过程称为切换(HO)。这会对服务质量(QoS)和体验质量(QoE)产生负面影响。因此,本调查的重点是探索和比较基于人工智能(AI)的智能HO解决方案,这些解决方案可以优化5G NR和6G网络中的HO。
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