## 7 Simulation and Evaluation  
Simulations were performed using OMNeT++ [37] integrated with SUMO [38] (Simulation of Urban MObility) and the netframework to emulate realistic urban MANET and VANET scenarios. SUMO managed the vehicular traffic within the city, reflecting accurate vehicle movements and interactions, while inet supported advanced network simulations using the 802.11 ax HE mode discussed in [39]. In order to accurately model radio signal propagation and the effect of urban environments, the RicianFading path loss model provided by the inet framework was used to account for both dominant line-of-sight signals and multiple reflected signals typical of urban settings. Additionally, the obstacles.xml configuration file was employed to emulate the presence of physical obstacles such as buildings and other structures, further enhancing the realism of the simulation. The simulations were configured to reflect the unique traffic and networking conditions of the Metropolitan area of Reggio Calabria (Italy). The global geo-coordinates for the four corners of the simulation area are x1: (38.09, 15.63), x2: (38.11, 15.64), x3: (38.94, 15.66), x4: (38.11, 15.66). Within this area, vehicles dynamically entered and exited the simulation, simulating real-world urban vehicular mobility patterns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Path** & **Routes** & **Total SF** \\ \hline A \(\rightarrow\) B \(\rightarrow\) J & r1 + r6 & \(0.5429+0.5429=1.0858\) \\ A \(\rightarrow\) C \(\rightarrow\) J & r2 + r7 & \(0.5500+0.5500=1.1000\) \\ A \(\rightarrow\) D \(\rightarrow\) J & r3 + r8 & \(0.4700+0.4700=0.9400\) \\ A \(\rightarrow\) B \(\rightarrow\) E \(\rightarrow\) J & r1 + r4 + r9 & \(0.5429+0.675833+0.675833=1.894566\) \\ A \(\rightarrow\) C \(\rightarrow\) F \(\rightarrow\) J & r2 + r5 + r10 & \(0.5500+0.530000+0.530000=1.610000\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Paths, Routes, and Total Scoring Functions (SF).  
Figure 6: Network topology diagram illustrating the routes from Node A to Node J with respective metrics.  
In this setup, the radio transmission ranges for RSUs were set to 500 m, while mobile nodes (vehicles) had a transmission range of 250 m, consistent with 802.11 ax standards. Communication patterns were defined by a random communication model, in which vehicles dynamically selected another vehicle or Road-Side Unit (RSU) as their communication destination. This setup simulated vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in an urban environment.  
A centralized controller was responsible for managing data collection, processing, and updating the machine learning models used for routing decisions in the MMS-DSR protocol. The controller received real-time data from RSUs, analyzed them, and updated the multi-metric scoring and beamforming models every 10 s to reflect changes in traffic conditions and network dynamics. The overhead of the centralized control involved data processing and model updating, which was feasible for small to medium-sized cities such as Reggio Calabria.  
The RSUs were strategically placed at major intersections and areas of high vehicle density to maximize MU-MIMO gains and beamforming efficiency, ensuring optimal coverage and minimal communication disruptions. The placement was determined based on traffic flow data, highlighting regions of frequent congestion and high vehicle mobility, making these locations critical for efficient traffic management.  
In the considered scenario, a total of 15 RSUs were deployed. These units were crucial for the network's infrastructure, particularly for managing the high-speed vehicular communication required in urban settings. The RSUs were equipped with advanced MU-MIMO systems, enabling significant enhancements in signal directivity and strength through beamforming techniques. Table 11 includes the Simulation Parameter set.