## 5 Performance Evaluation
### Multihop D2D Broadcasting in Urban Vehicular Networks  
We now discuss how MDB performs in a vehicular network. In this scenario, moving vehicles should be able to exchange information (like alert messages or traffic updates) with other vehicles, roadside elements (e.g., traffic lights) and/or pedestrians in a fast and efficient way. The inherent proximity of the communications makes D2D transmissions (possibly exploiting multihop transmissions) one of the key enabling technologies for these services. Figure 24: 95 th percentile of delay with non-cooperative UEs. Figure 25: Avg. allocated RBs per broadcast with non-cooperative UEs. For this reason, we recently enhanced SimuLTE so as to make it interoperable with Veins [39; 40], an OMNeT++-based framework for the simulation of vehicular networks, which is widely used by the research community. This way, it is possible to endow vehicles with an LTE NIC, immersing them into a cellular infrastructure where they can communicate with other network elements and/or among them, possibly exploiting D2D transmissions. We consider the scenario depicted in Figure 26, which is taken from the 3 GPP specifications [41]. The latter describes an urban scenario, defined as a grid of size 250 m \(\times\) 433 m. Streets have two lanes per direction and each lane is 3.5 m wide. Inter-site distance between eNBs is 500 m. With reference to Figure 26, we defined four bidirectional vehicle flows, respectively connecting points A-B, C-D, E-F and G-H. From each entry point A to H, a new vehicle enters the network each 2.5 s. Vehicles are attached to best serving eNB according to a best-Reference-Signal-Received-Power (RSRP) criterion when they enter the grid, and they can perform handover to another eNB when they perceive a better RSRP. Vehicles moves at a speed of 60 Km/h. This means that the distance between vehicles in the same lane is 41.67 m. As shown in [42], these values for speed and distance correspond to a non-rush-hour scenario, although they are not very dissimilar to a rush-hour case, where speed and distance in the rush-hour are respectively 36 Km/h and 20 m. Physical layer parameters are the same as the previous section and presented in Table 1. We will analyze the broadcast delay with increasing target radius. For each configuration, we run a simulation for 250 s and, on each second, a randomly chosen vehicle generates one message and starts the broadcasting. Since vehicles appear in the network dynamically (hence, there are no vehicles in the network at the beginning of the simulation), we start gathering statistics after a warm-up period of 50 s.  
The Trickle suppression mechanism is enabled and vehicles relay a message if they are within the target area defined within the message. MaxC/I is employed as the scheduling algorithm, hence frequency reuse is not enabled. Resource allocation is performed according to the SRA mode, which proved to be more efficient, with vehicles transmitting using CQI = 7. Figure 26: Urban grid scenario. Figures 27 and 28 shows respectively the average and the 95 th percentile of the delay required to complete the broadcasting, as a function of the target radius. By comparing the results with those obtained in the previous section, we can observe that both average and 95 th percentile of the delay are higher in the vehicular case, although not overly so. This is due to the particular deployment of the vehicles and the definition of the target area as a circle. With reference to Figure 29, we assume that the circled vehicle (bottom right of the figure) generates a message to be broadcast within the area defined by the dashed circle. In order to reach the vehicle highlighted by the square, the message needs to traverse several hops along the roads, since it is too far to be reached with a single, direct transmission and there are no other vehicles along the straight path between them. Figure 28: 95 th percentile of broadcasting delay. Figure 27: Average broadcasting delay.