# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 5 ML for Mobility Prediction  
Beyond 5G systems will be heterogeneous, ultra dense, and with large dimension, so the mobility prediction will likely be the most formidable case of beyond 5G systems due to the continuous handovers. Mobility prediction can be achieved intelligently by applying ML approaches. Handover efficiency can be difficult in managing the URLLC, eMBB, and MTC requirements. It may also become harder to track the locations due to frequent mobility. RL can be one of the ML approaches to achieve the handover efficiency intelligently by using a centralized RL agent [32]. UE measurement reports (reference signal received power (RSRP) and reference signal received quality (RSRQ)) are handledby centralized RL agent, as it learns through a trial-and-error learning approach, which enhances its capability to take handover decisions based on RL framework. In order to enhance the selection of target cell during handover (HO) decision, another neural network based ML approach was proposed in [33]. Two levels of a neural network model have been proposed to determine the probability of successful downloads and average download time. In the first level as input, UE measurement reports can be used to train neural network, and collects the previous information on the quality of experience (QoE) (download completed/not completed) as output. In the second level, those UE measurement reports with completed download are used for training. After the completion of the training, trained neural networks have been used by the handover algorithm to estimate the expected QoE. Next generation network (NGN) should meet mobility requirements of vehicular networks in heterogeneous environment. There can be frequent handovers due to mobility of high speed vehicles. In [34], the Markov-chain technique is proposed to estimate the possible location of the UE and to reduce the numbers of handover. They proposed two handover strategies, proactive and reactive handovers, and also analyzed the user's random movement across multiple cells. Mathematically representation is as follows:  
\[p_{n}=[p]\times[P_{n-1}]=[p_{n-1}]\times[P] \tag{3}\]  
where \(n\) illustrates the number of transitions, \(p_{n}\) is the probability of user's location, \(p\) and \(P_{n-1}\) are the initial and current transition matrix, respectively, \(p_{n-1}\) represents the initial distribution after \(n\), \(P\) is denoted as the initial transition matrix. Even if the user finds an RSRP with strong signal strength in its current cell, reactive handover strategy helps to reduce the unnecessary handovers. User do not need to verify its current cell RSRP and the handover is carried out at any time in the proactive handover strategy. Signaling overhead can be increased in micro cell environment due to consistent handovers (HOs). The HO scheme in the existing RAN architecture comprises redirecting control and data channels from one base station (BS) to another, which results in increasing the signaling overhead load. In [35], authors proposed an efficient and intelligent core-network (CN) radio resource control (RRC) signaling mechanism based on control/data separation architecture (CDSA) in order to reduce the HO signaling latency. The key idea behind the CDSA approach is to split the control signals from the data signals, providing only required signaling to support coverage [36]. CDSA separation architecture has been shown in Figure 5.  
Signaling related to data transmission is provided on request depending on the number of active users. Control base stations (CBS) are deployed to ensure the network  
Figure 5: Control/data separation architecture. connectivity and within the CBS footprint, data base stations (DBS) are deployed to provide high data rates. The CDSA provides flexibility compared to the conventional RAN architecture in implementing mobility prediction for HO management schemes. In the proposed model [35], The Markov-chain model is used to predict the future DBS HOs.