# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 3 Related Works
### Cloud/Edge-Based Approach  
The industry was the major force behind the push for many cloud and edge-based solutions. This has also applied to handover solutions, such as cloud and edge computing, and can provide significant benefits in terms of lower latency and higher throughput when compared to systems that do not utilize them.  
This can be observed in [22], where the authors addressed the issues with cooperative interference mitigation and handover management in heterogeneous cloud small cell networks (HCSNet). This is a type of network architecture that combines the cloud radio access network (RAN) with small cells. The authors specifically target UEs moving between macro cells and small cells. A low complexity handover management scheme was proposed, and its signaling procedure was analyzed. The authors developed their algorithm based on UE speed estimations (using an autocorrelation function) and UE latency requirements. Additionally, to avoid user interference at the cell's edge, the authors proposed a coordinated multipoint (CoMP) joint transmission clustering scheme using affinity propagation methods. The results show that the signaling overhead related to call holding time and high mobility users are reduced significantly by 40% and 90%, respectively. Although the solution provides an effective reduction in signaling overheads, there is a lack of analysis of other key performance indicators such as the number of handovers, ping-pong rate, and handover latency.  
In comparison to the previous effort, the authors in [23] focus on the latency benefits of using a cloud RAN architecture, as this is an important enabler for URLLC services for high mobility applications. The authors analyzed the performances of different cloud RAN architectures and then developed a new concept called early admission control (EAC) for reducing the handover preparation time. This algorithm is created with respect to synchronous handovers without random access and then compared to the current distributed RAN configurations. The results show a clear reduction in handover preparation time of up to 30% when compared to cloud RAN architectures without EAC and more than 60% when compared to distributed RAN architectures (resulting in better throughput and lower signaling overheads). Although this proposed approach is promising, it was observed that the authors could have moved the EAC preparation closer to the edge of the RAN in order to achieve an even lower latency, which is the concept being investigated in this paper.  
In [24], the authors considered a 5G-MEC network where MEC servers are co-located with 5G BSs to support the computation-intensive, and delay-sensitive mobile augmented reality (MAR) applications. They proposed a handover scheme for users of MAR applications that considered not only the RSS of BSs but also the computation load of the co-located MEC servers. Hence, handovers can be triggered when the RSS of the serving BS is sufficiently degraded or when its co-located MEC server is sufficiently overloaded. It was shown that the proposed scheme could improve UE experienced delay significantly and is relatively robust to different UE speeds. However, it is conceivable that the proposed scheme can also cause UEs to be handed over to BSs with less loaded MEC servers as well as lower RSS, resulting in a deteriorated performance of non-MEC applications that may be executing on these UEs.