# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 1 Introduction  
With the introduction of the fifth-generation (5G) cellular network [1], the industry is posed with many diverse challenges. A common challenge to all three major pillars of 5G (enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low latency communications (URLLC)) is seamless and low latency multi-tier handover. In the latest 3 rd generation partnership project (3 GPP) standards for 5G [2], it was noticed that the event-based triggering for handover ignores various key elements of the user's session that require to be taken into consideration, such as their mobilities and data rate requirements. The user requirements are ever-changing; thus, cellular networks must be sufficiently dynamic to react and cater to this demand effectively. There are various channel inefficiencies that occur when a diverse range of requirements are not taken into consideration.  
The issues relating to multi-tier handovers have not been effectively resolved to this day. There is literature addressing the issues of handover, but only a small portion of these adopt a form of artificial intelligence (AI) or cloud computing techniques in their solutions. The use of deep learning (DL) and multi-access edge computing (MEC) for optimizing handover is still a gap in the industry that has not been explored yet.  
With the addition of MEC and DL in 5G, network operators can gather user data and analyze variations in signal strength, mobility patterns, and data rate requirements of each user to achieve optimum user experience. Additionally, with the implementation of a system that understands the user's requirements, network elements also benefit because this helps to manage the base station's resources efficiently. Keep in mind that the network operators should extract or use all of these data without compromising on the compliance of user privacy laws of the specific country.  
The objective of this paper is to develop a DL handover decision algorithm while utilizing MEC. This will enable a faster and more reliable handover system that would ideally allow the user to switch seamlessly between any cellular network configurations based on key requirements. The scope of this research is focused on a 5G heterogeneous environment with various base station tiers (macro, micro, and femtocells) to carry out a comprehensive multi-tier handover evaluation. This research is validated by conducting software simulations to compare the proposed method to the handover technique specified by 3 GPP in the technical standard (TS) 38.300 [3]. Both key components of the simulator: channel model and scheduler, are compliant with 3 GPP standards 38.104 [4] and 36.873 [5], respectively. The main contributions of this paper are as follow:  
* Proposal of a new DL Long Short-Term Memory (LSTM) handover decision algorithm that uses a look-up table (LUT) and is catered to key user quality of experience (QoE) and quality of service (QoS) requirements;
* Replacement of the time to trigger (TTT) with a dynamic LUT-based trigger mechanism;
* Modification of the handover admission control process when using the DL LSTM logic to occur at the same time instant that the base station sends the handover command to the UE. This is assuming that the user plane function (UPF) and the access and mobility management function (AMF) is located at the MEC aggregated edge.  
The rest of this paper is organized as follows: Section 2 provides some necessary preliminaries. Section 3 overviews the related works. Section 4 introduces the system model. Section 5 presents the proposed algorithm. The simulation model and performance metrics are described in Section 6. This is followed by results and discussion in Section 7. Finally, Section 8 concludes the paper with some directions for future work.