Search for: [Description = "Ensuring a seamless connection with various types of mobile user equipment \(UE\) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high\-band spectrum – such as the millimeter wave \(mmWave\) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new \(small\) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long\-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks \(HetNets\). As a result, the number of passthrough cells using the handover \(HO\) process will be dramatically increased. Mobility management \(MM\) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, flexibility, and scalability.Thus, smart algorithms and techniques are required in future networks. Also, machine learning \(ML\) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications \(mMTC\), enhanced mobile broadband \(eMBB\), and ultra\-reliable low latency communications \(uRLLC\). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond."]
