# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 3 Related Works
### Multi-Connectivity Approach  
Multi-connectivity refers to the situation when a user is connected to more than one BS to ensure that its connection to the network is not lost. Soft handovers, considered in this paper, are a type of multi-connectivity, as when handing over, the user is connected to multiple BSs at once for a small amount of time. In the following, we review the literature that employs multi-connectivity for multi-tier handovers.  
In [15], the issue of high handover latency and signaling overhead was addressed. The authors proposed that the UE is always connected to two 5G millimeter-wave access points (mmAPs) and one LTE BS. The proposed scheme compares the predicted RSS of the active set of 5G-mmAPs and one LTE-BS to another candidate, LTE-BS or 5G-mmAP. If  
Figure 1: Edge (MEC) and Central (Cloud) data centers.  
the candidate device has a better RSS, the network will initiate a handover request to the core network. The proposed algorithm is compared to two systems: A3 event-based logic and the current scheme but without prediction. Simulation results show that the average throughput of the proposed scheme is improved by 12% under moderately high mobility (30-50 km/h). Additionally, the number of handovers is also reduced by 5%. However, it is observed that although the system is a multi-tier network, it lacks a detailed study on multi-tier handover as the focus is primarily on the 5G-mmAPs. It is also observed that the resulting channel efficiency is reduced since the UE uses three forms of connectivity to achieve minor improvements. This issue can pose a major constraint when this is deployed in a populated environment.  
The same channel efficiency and signaling overhead issues are accentuated in [16], where the authors aim to address the issue of mm-waves being highly susceptible to blockages and degradation in channel quality. The proposed solution consists of a heterogeneous dual-connectivity solution that connects UEs to both 4G LTE and 5G mm-wave BSs, providing rapid switching from 5G to 4G for failures on any link. Additionally, the solution added the complexity of including static and dynamic TTT delays. The proposed algorithm is compared with that of 3 GPP for vertical handover. In terms of the number of handovers executed, the 3 GPP algorithm is more efficient by 5-10%, as the proposed dual-connectivity approach requires the handovers to occur more frequently as user requirements change. However, the handover latency is reduced by 70%, while the overall throughput is marginally improved by 2-5%.  
In [17], the authors proposed a combination of soft and hard handover solutions for horizontal and vertical handover in a vehicular network. The proposed algorithm uses circular geometric calculations to model the cell's coverage, relying on the vehicle's GPS coordinates to trace the path accurately. It also uses soft handover between roadside units (RSUs) and hard handover between RSU and BS. Additionally, the handover latency is considered when the algorithm executes its decision, resulting in a combination of the cell with the lowest latency and the best QoS. The proposed solution is compared with the 3 GPP threshold and signal hysteresis methods to determine if the number of handovers and HOFs are reduced. The algorithm provided a reduction of 30% in handovers and a 25% reduction in HOFs at speeds of 100 km/h. However, there are two potential drawbacks to this proposed solution: (i) The signal overhead for RSU and BS will increase dramatically as the density of vehicles grows. Hence, the equipment cost on the network side to support this will increase. (ii) The system present in the vehicle requires at least three other separate systems to be able to execute handovers, which would further add to the cost of the system.