AI-Based Approaches for Handover Optimization in 5G New Radio and 6G Wireless Networks
AI-Based Approaches for Handover Optimization in 5G New Radio and 6G Wireless Networks
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DOI：
10.1109/iccosite57641.2023.10127687
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Ahmed F. Ashour;M. Fouda
中科院分区：
文献类型：
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作者：
Ahmed F. Ashour;M. Fouda
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，因此需要部署小蜂窝。这反过来又需要安装大量的基站来覆盖整个地区。这种设置中的大量小区和用户可能会在用户切换小区时导致呼叫中断，这一过程称为切换(HO)。这对服务质量(QOS)和体验质量(QOE)有负面影响。因此，本次调查重点探索和比较了基于人工智能(AI)的智能HO解决方案，这些解决方案可以在5G NR和6G网络中优化HO。
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.