There are several proposals [11; 12; 13] that use ML schemes to perform channel estimation and equalization, but these proposals do not consider the 802.11p amendment. In other words, these studies can not be extrapolated for VCSs because they do not consider the characteristics of the non-WSSUS mobile-to-mobile channel or the particular frame structure of the communication packet; therefore, the channel estimation must be performed differently to the 802.11p amendment mandates. In [11], [PERSON] and [PERSON] used a Deep Learning (DL) approach characterized by off-line training to perform channel equalization and estimation for an OFDM-based system, by showing a performance improvement over the LS and the Minimum Mean-Square Error (MMSE) schemes. The work presented in [11] shows that DL has the potential to improve the estimation equalization of communication channel in OFDM-based schemes. However, when considering that V2V safety applications have latency limitations, and since the channel has rapid variations in the time and frequency domain, an off-line approach should be discarded, as the channel models used in the training stage might differ from the actual channel. In [12], [PERSON] et al. proposed an artificial neural network (ANN) scheme based on a convolutional neural network to perform channel estimation on a Cellular Vehicle-to-Everything (C-V2X) communication system. This technique performs well but it again requires an off-line learning stage. In [13], [PERSON] et al. introduces the use of a fully Complex Extreme Learning Machine (C-ELM) neural network to perform channel estimation and equalization on OFDM signals, where an exponential channel model with eight stochastic fading delay paths based on the IEEE 802.11g standard (mainly, a frequency-selective channel) is considered. The results show that the proposed C-ELM algorithm outperforms other state-of-the-art techniques, such as MMSE, LS, and a deep neural network-based scheme, and has a better generalization ability by considering different subcarrier modulation formats and numbers of subcarriers. It is worth noting that only the hyper-parameters (mapping function and number of hidden neurons) of the C-ELM are reported; no theoretical or simulation justifications are presented. In this work, as an initial exploration, we propose the use of an Extreme Learning Machine (ELM) channel estimator and equalizer technique, which is characterized by Semi-Supervised (SS) learning for vehicular communications. The proposed algorithm considers the special propagation conditions in the time and frequency domains of vehicular communication channels. The main contributions of this work can be summarized as follows:  
* We propose a regularized ELM subject to SS learning as channel estimator and equalizer to enhance the performance of a representative IEEE 802.11p OFDM-based system in terms of Bit Error Rate (BER). To this end, we add a novel parameter denoted by \(\delta\) in the Semi-supervised Extreme Learning Machine (SS-ELM) to address the time-domain fluctuations of the channel. Furthermore, a frequency-domain localized mapping is used to properly recover the OFDM signal, namely to address the frequency-selective channel;
* Taking the simulation framework of the evaluated system into account, we compute the sub-optimal SS-ELM hyper-parameters to diminish BER via extensive simulations. We also show that a supervised ELM does not improve the BER performance of a vehicular IEEE 802.11p system;
* We compare the proposed technique with current state-of-the-art machine-learning-based channel estimation schemes as well as traditional techniques in an urban environment for several values of Energy per Bit to Noise Power Spectral Density Ratios (\(E_{b}/N_{0}\)). The addressed techniques are also contrasted in terms of the required processing time.