# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 3 Related Works
### AI Approach  
AI-based handover is where the system learns user patterns and dependencies and attempts to find the most optimal solution through its learning. AI can include forms of ML as well as other forms of evolutionary learning. This section divulges details of the improvements that AI-based schemes can provide for multi-tier handovers. The authors of [18] proposed to use fuzzy logic to solve issues relating to redundant handovers and HOF ratios in dense small cell networks in LTE. A self-optimizing system that analyses the user velocity and radio channel quality and adapts hysteresis values for handover decisions is proposed. The inputs for the system are the user velocity, RSRP, and reference signal received quality (RSRQ). The proposed algorithm is compared to four other algorithms: Best Connection, Conventional LTE handover, Fuzzy Multiple-Criteria Cell Selection integrated with TOPSIS, and Self-Tuning Handover Algorithm. The proposed algorithm reduced the average number of handovers by 20%, the overall HOF ratio by 25%, and the ping-pongs events by 50%. However, the impact on latency and throughput has not been analyzed, which could insinuate a possible increase in computational strains, resulting in a reduction in the user's quality of experience. In [19], the authors addressed the inefficiencies of handover for in-building systems. The proposal is to optimize these inefficiencies through ML and data mining techniques by developing a clustering algorithm based on shapelets and wavelet decompositions at the cell's edge. The considered environment consists of two in-building systems on a university campus and a three-sector macro-cell. The authors developed objective functions for three scenarios of loading the macro-cell and in-building systems (assuming UEs are exiting the building) to achieve the optimal operating point, which is a combination of the A2 and A3 handover thresholds and a TTT period. The optimal operating points were based on HOF, ping-pong, and average data rates. The achieved data rate gain by this algorithm is between 25% and 65% over the static A3 algorithm. The authors discussed ping-pong and HOF rates but did not provide evidence of improvements in these areas. It is also unclear how the proposed algorithm would perform when there are mass user movements, such as leaving the building when a class finishes. A DL approach was analyzed in [20], where the authors proposed to significantly reduce service traffic that is transmitted through the 5G communication channels and to optimize handovers. The proposed algorithm uses gated recurrent units (GRUs) to provide a rapid response to changes in the environment. The GRU is used to predict how many users would be in a particular cell for a given the time of day. The prediction scheme varies the size of the cell coverages based on the time of day. This allows underloaded cells to be easily handed over and overloaded cells to become harder to connect into. The authors used supervised learning for these predictions and compared them to a DL LSTM over 300 epochs. It is shown that the GRU can achieve a better result than the LSTM in a short time frame, although the LSTM becomes more accurate as the number of epochs increases. It is also shown that the GRU can accurately model daily user traffic with an accuracy of 90%. However, the authors did not state when all cells are overloaded, which would cause the coverages to become so small that dead zones appear, causing mass radio link failures for the users. In [21], the authors proposed a hybrid user mobility prediction approach based on vector autoregression (VAR) and gated recurrent unit (GRU). The proposed approach is shown to predict user future trajectory with less error than methods based on GRU alone, recurrent neural network (RNN), and LSTM. The approach is then applied to handover management in mobile networks to reduce the amount of handover processing and transmission costs. However, user mobility is only one factor that could affect the connectivity of the users to the network.