###### Abstract  
The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.  
5G; machine learning; mobility management; small cells; IoT +
Footnote †: journal: _Applied_  
of interference and signals blockage by large obstacles [6]. In campus/concerts type, the focus is the gathering places like seminar halls, concerts galleries, and hallways around meeting/classrooms etc. while in the case of stadiums such as football, tennis, cricket, rugby, and arena require high data rate for upcoming virtual, augmented, and mixed reality services.  
Multiple technologies for 5G communication include massive multiple input multiple output (mMIMO), millimeter wave communication (mmWave), self-organized network (SON) and ultra-dense network (UDN) supports all these mentioned scenarios [7; 8]. In 5G technologies, service operators and vendors experience different challenges for HO implementation. First challenge is imprecise signal measurement in which the bandwidth and frequency of mmWave is high which is essential to increase the capacity of network communication system. The signal path loss becomes high at high frequency bands due to atmospheric conditions, low diffraction around the walls or obstacles and rain absorption. These factors reduce the range of the signal. Moreover, signals operating at high frequency band can become a victim of fading easily which in turn creates error, lowering the overall switching rate of HO or unnecessary roaming. At all, it will reduce the customer's experience. The next challenge is immediate HO where the size of the radius cell is smaller than the standard size in the UDN architecture. In every small cell, the total time interval of the user equipment becomes relatively small and caused frequent HOs. As a result, the overlapping time of two terminals also becomes relatively low. At last, 5G does not contain same network layers and technologies in contrast to 4G and 3G network communication and both vertical and horizontal HO process embrace HO issues in 5G [9; 10]. Beside these challenges, there are number of other challenges exists due to heterogeneous communication behavior of network, merging of 5G technologies, and optimized automation of existing processes. Figure 1 shows the 5G NR communication system under the consideration of heterogeneous environment and required use cases i.e., eMBB, uRLLC, mMTC.  
Figure 1: 5G NR Communication System.