## 4 Simulation Results and Discussions
### Execution Time Analysis  
In this Subsection, we evaluate the performance of the addressed techniques in terms of execution time, which is the time required by the algorithms to complete the processing (from the FFT module to the parallel to serial converter, see Figure 2). As the execution time is hardware/software dependent, Table 3 is presented to show the hardware configuration used in the simulations. Regarding software features, Matlab R2017a has been used to perform the evaluation. To measure the representative interval times, we follow the Monte Carlo simulation explained at the beginning of Section 4 by fixing the channel model parameters at arbitrary values, since these do not interfere in the estimation of the processing times. Because each of the frequency slots shown in Figure 6 has a localized submapping estimation and equalization, the slots can be processed independently from each other, thus decreasing the execution time of the proposed SS-ELM scheme by a factor of 4 due to the number of pilots in an OFDM symbol (refer to Figure 6). The results of this experiment are depicted in Table 4 under the name of parallel SS-ELM.  
Figure 14: BER as a function of \(E_{b}/N_{0}\) for different evaluated techniques and system configuration 1.  
Figure 15: BER as a function of \(E_{b}/N_{0}\) for different evaluated techniques and system configuration 2.  
The results of Table 4 depict that the proposed SS-ELM increases the execution time by around 19 times compared to the CDP estimator for instance, and when optimized with parallel computing, the execution time is reduced to \(167\pm 2.6\) ms, increasing the time to approximately nine times. Note that although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far from the upper limit of latency equal to 300 ms [5] specified by ETSI for safety applications. It is important to consider that these results are software/hardware-dependent, so the results may vary from device to device. Finally, considering that the computer processing units that modern cars have on board are powerful, the overall results of the proposed algorithm are sufficient to justify an increase in the execution time. This is especially important for safety applications in vehicular communications, where a low BER is vital to ensure the reliability of the information. Considering the execution time. the proposed algorithm is suitable for safety applications such as collision risk warning or road hazard signaling [5]. However, its use for critical safety applications such as pre-crash sensing should be investigated further.  
Note that although the proposed SS-ELM scheme has the longest execution time with respect to the compared models, we conclude that it is possible to further reduce the learning and/or operation time of the scheme by having only one layer forward, e.g., on an Field-Programmable Gate Array (FPGA) [32]. In future work, we will explore the use of FPGAs to implement the proposed scheme, analyse the impact of a larger sample size of \(\delta\), the possibility of applying unsupervised configurations as an alternative to semi-supervised ones, and analyse the impact of using channel coding schemes in different system configurations.