## 4 Simulation Scenario with Ultra-Dense Urban Environment  
In this research, a Python-based HetNet simulator was modeled based on 5G Evolution 3 GPP Rel.17 specifications. The simulator allows for the creation of diverse network environments, including rural, urban, and suburban scenarios. It supports both macrocells and microcells that are operating in sub-6 GHz and mmWave frequency bands. The UE mobility is incorporated, allowing for UEs to move randomly within the simulated environment layouts, connect to neighboring cells, and perform the handover process based on A1, A2, and A3 events. The simulator automatically generates any specific area layouts based on its coordinates. Then, it distributes the UEs uniformly within this area as shown in Figure 5. Moreover, it accurately captures the network's dynamic processes, simulating processes at the milliseconds level. This enables the evaluation of network performance metrics, particularly under different conditions such as HetNet implementation and a high user density. Thus, the evaluation highlights the effectiveness of the MM metrics such as HO, HOF, RLF, and HOPP rates and the impacts of network load distribution among the cells in a dense urban environment. Each hexagonal-shaped cell is divided into three 120\({}^{\circ}\) sectors for further network efficiency. This configuration ensures user location awareness, improves the connectivity of users, and enhances system performance. These cells operate at different carrier frequencies and channel bandwidths to minimize the effects of interference. The radius of each cell can vary based on cell type; macro-cells typically range from 500 m to 1200 m, while micro-cells cover 200 m to 500 m. The mobile users are spawned at random coordinates, destinations, and speeds. The speeds vary based on the simulated environment (e.g., city center: 3 to 20 km/h). To represent a real network environment, each user can stop for a random duration and then resume moving until reaching the simulated environment's edge. Otherwise, the user is removed and a new one with a new configuration is spawned. Additionally, at regular intervals, several users are created with random edge coordinates, potentially entering the central area. The simulation scenario focuses on an ultra-dense urban environment, specifically the city center of Karbala, Iraq. Karbala shows significant seasonal fluctuations in user density patterns over the year. Additionally, the city hosts annual large religious gatherings that last for a few weeks, during which visitor numbers reach their peak with millions of visitors. This causes significant congestion that impacts all infrastructure, particularly wireless networks. To reflect this real-world scenario with resource limitations, the simulation deploys a limited number of UEs (around 400) concentrated in the simulated area. Some UEs with low-speed rates simulate co-located pedestrians using a pedestrian mobility model. The user data traffic model incorporates various data rates between 15 Mbps and 35 Mbps, reflecting typical usage patterns and potential network congestion. Therefore, seven 5G cells (differently colored as shown in Figure 6) are located centrally to evaluate the performance of the DDLB algorithm. Most users are required to pass through the center of the simulated environment to allow for the consideration of various HO scenarios as well as additional possibilities with different load situations among the chosen cells, especially the center cell. Being aware of all limitations related to simulation-based experiments, we suppose that the considered scenario (Karbala city) could be treated as a good representative of other similar locations where an extremely high density of users can be noticed. Table 1 summarizes the main parameters used in the simulation scenario. In each simulation cycle, users move toward their destinations. If a data session exists, the user checks various events related to the session state such as disconnection time, channel quality, RSRP, and MM events.