## 4 Simulation Results and Discussions  
To further validate our proposal (SS-ELM), we evaluated other state-of-the-art schemes; these techniques are LS, STA, CDP, and C-ELM [13]. For the case of the LS, STA, and CDP schemes, Zero Forcing (ZF) is used as the channel equalize [23]. For comparison results, note that among the several ML-based approaches reported in the literature, we included the C-ELM method, as it has shown the best BER performances without computational cost, not only for communications through optical fiber [21; 22] but also for advanced wireless communication systems [13; 24; 25; 26; 27]. Of course, these works are not focused on IEEE 802.11p-based V2V communication systems, which present a harsh non-stationary, time-varying, frequency-selective channel; refer to Section 2. On the other hand, it is worth noting that our proposal works with strictly real data (a single constellation symbol is divided into its quadrature and in-phase parts) and consists of a semi-supervised machine learning (a combination of supervised and unsupervised learning). While the C-ELM [13] represents fully complex neural networks under only supervised learning. Furthermore, the C-ELM neural network needs a bounded and differentiable activation function defined in the complex plane and cannot follow semi-supervised training, which limits its generalization ability. Each one of these models is tested in two different system setups. Further details regarding the system setups are given in the subsequent lines. A Monte Carlo simulation of 21 iterations is used to ensure statistical regularity. A total of 10.000 data frames are transmitted in each iteration, and \(E_{b}/N_{0}\) ranging between 0 to 20 dB that correspond to real-life scenarios [28] are used in the evaluations. The parameters used to perform the simulations are shown in Tables 1 and 2. Specifically, Table 1 illustrates the IEEE 802.11p system parameters for a 10 MHz channel, whereas Table 2 shows the evaluated system configurations.  
Both system configurations simulate an urban environment, with speeds lower than 50 km/h. The first configuration depicts a scenario in which the transmitter is accelerating and changing lanes in preparation to overtake the receiver [29], whereas the second configuration is a scenario in which the receiver is taking an intersection while the transmitter remains in the same lane. For clarification purposes, the parameters that originate the overtake and intersection scenarios (the movement angles, acceleration angles, and velocities of the transmitter and receiver, and initial distance) are shown in bold in Table 2. In this sense, these parameters are highlighted with a red color in Figure 5, where they can also be easily distinguished. Furthermore, the simulations are done with Binary Phase Shift Keying (BPSK). This is because, with BPSK, we have the slowest data rates, as well as the most robust transmissions, which are critical in safety applications. To conclude this part, in order to find the sub-optimal hyper-parameters of the SS-ELM and compare the performance of the different models, we will present the results as a function of the BER and \(E_{b}/N_{0}\).