# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 6 Simulation Environment and Performance Metrics  
### Simulation Environment  
The simulation tool used is the Vienna 5G system-level simulator [28], which we augmented with several advanced toolboxes from MATLAB. The BS and UE scheduler used is a 5G new radio scheduler available from the 5G toolbox of MATLAB [29]. This scheduler is compliant with the 3 GPP standards and combined with the Vienna simulator to develop a fully functioning uplink and downlink 5G system-level simulator. The round-robin scheduler mode was chosen as it has low complexity and provides long-term fairness for all users regardless of their priorities and CQIs.  
In terms of modeling user mobility, there are three categories of users considered in this simulation: pedestrians, cyclists, and motorists. Their mobilities are modeled using an application named Driving Scenario Designer, which requires the use of the automated driving toolbox of MATLAB [30].  
The handover logic and procedure implemented in this simulator follow key 3 GPP standards. The handover simulations involve a total of 40 mobile UEs, and each simulation run is conducted for 200 s. This duration excludes the initial 10 s \"warm-up\" phase of the simulation before results are recorded.  
The deep learning is simulated using the Deep Network Designer Application, which requires the use of the Deep Learning Toolbox of MATLAB [31]. The Adam solver is applied for training the DL system. The initial learning rate is set to 0.001, as a higher value creates a less accurate model, while a smaller value takes very long for the system to learn with little to no improvements. The gradient threshold is set to two to prevent the gradients from diverging from the desired learnings.  
The LSTM is taught to learn a sequence of 4 dimensions and 24 classifications, as described in Section 5. The number of epochs and hidden units is varied to find the optimal values. For the DL LSTM, a training and testing data set of 25,000 data points are taken based on simulated movements of 10 users (four motorists, four pedestrians, two cyclists): eight are used for training (20,000 points), two are used for testing the prediction accuracy (5000 points). BSs are only considered if their CQI \(\geq\) 1. User data point sizes varied from 2200 to 2700 based on the number of BS coverages that could be quantified as a potential base station.  
The region of interest is rectangular, spanning 600 m by 700 m (0.42 km\({}^{2}\)) with varying building heights between 10 and 45 m. The simulated region is based on New York University (NYU) with Manhattan-style building configurations, as shown in Figure 4a,b. Building widths and lengths are mapped in blocks of 25 m by 25 m. Street widths are 25 m wide, which can accommodate all types of users and split easily into pedestrian walkways (2.5 m on either side), cyclist ways (2.5 m on either side), and road lanes (7.5 m per lane: 4.0 m wide lane for moving vehicles, and 3.5 m wide land for off-street parking). The colored dots in Figure 4b depict the BSs. There is a total of 69 BSs: 2 macrocells (dark blue dots), 43 microcells (red dots), and 24 femtocells (teal dots).  
Figure 3: Flow diagram of the modified handover preparation phase.