Since the last decade, researchers have shown considerable interest in the field of IVC. The advancements in integrated circuit technology, efforts to make travel safer and more convenient, and increasing demand for on-the-go connectivity have fuelled the search for better solutions. Channel characterisation plays a pivotal role in developing a high-performance IVC system. A good number of literature dealt with intra-vehicular (iv) channel characterisation in public vehicles. In [
7], authors provided insight into the channel environment in the 5-GHz bands inside an aircraft. The authors designed the channel model by using the root mean square error (rmse) delay spread and coherence bandwidth. Trains are one of the primary public transport vehicles, but train wagons still lack high-capacity wireless networks. In [
8,
9], authors measured and analysed the intra-wagon channel in the 25–40-GHz band and the 60-GHz and 300-GHz bands using the ray tracing (rt) tool. An underground convoy was used for intra-wagon frequency-domain measurements in the 26-, 28-, and 38-GHz bands [
10]. The authors concluded that antenna position and scattering-rich environment are essential in intra-wagon scenarios. The authors also advocated that the waveguide effect and human blockage affect the channel measurements and must be considered while designing the model. In another intra-wagon channel measurement campaign, authors [
11] compared the channel-sounding data with RT in the 300-GHz band. Another extensively used public vehicle anywhere around the world is the bus. In [
12], Semkin et al. constructed a logarithmic, distance-based pathloss model for wearable deployments inside a bus in a 60-GHz band. For the same 60-GHz band, Chandra et al. reported frequency-domain measurements inside the bus. Here, the authors [
5,
13,
14] proposed PDP-based analytical model for mmWave intra-bus wireless channel. Apart from public vehicles, private vehicles like cars and SUVs are the most extensively researched. With the evolution of human-driven to driverless cars and fossil fuel-based to electric vehicles, manufacturers and researchers are working in tandem to provide better safety and the best user experience. In [
15], authors developed a comprehensive simulation framework to estimate frequency-domain channel transfer function in the intra-car scenario for the UWB band. In a similar attempt, authors in [
16] concluded that for UWB band intra-car scenarios, the cluster arrival rate is higher than that of indoor propagation. In contrast, the cluster decay rate is lower than the indoor propagation scenarios. The following articles [
17‐
21] compared the performance of intra-vehicular schemes for UWB and mmWave bands. In [
20], analysed the time of arrival of the packets and concluded that in the absence of human blockage, ranging accuracy is similar in both bands, but in the presence of passengers, ranging accuracy got reduced for the mmWave band, whereas [
17,
18] advocated that the 60-GHz mmWave band performs better than UWB in the intra-car scenario. Authors in the intra-vehicular studies are still running; it is proved that mmWave-based channel characterisation performs better than UWB. Also, the studies are predominantly specific vehicle dependent and cannot be extended to new scenarios.
Machine learning is expected to extract channel characteristics and design and estimate a channel model much better than conventional methods. An overview of the ML-based framework for channel characterisation and modelling was provided in [
22]. The article advocated the use of reinforcement learning for channel modelling in autonomous vehicles and further emphasised searching for more generalisable schemes. Indoor channel sounding at 100 GHz was analysed by [
23]. The authors proposed fingerprint-based feature extraction from PDP and then the application of various ML algorithms to design a channel model. A hybrid approach of physics based and data driven to generalise the site-specific through-vegetation scenarios was proposed in [
24]. The authors proved that ML could estimate the complex mmWave channel parameters accurately based on the channel geometric configurations and
\(T_X\)‐
\(R_X\) positions. In the articles [
25‐
29], the authors elaborated on the use of ML in other application domains justifying the use of ML-based schemes in mmWave channel characterisation and channel modelling. In the articles [
6,
30], the authors provided an extensive survey for the application of ANN-based artificial intelligence in channel characterisation and channel modelling. The authors in [
31‐
33] provided a step-by-step tutorial for optimising the neural network followed by the application of MLP-based ANN and convolutional neural network to extract channel characteristics and used the trained model for channel modelling.
The use of artificial intelligence and machine learning (AI/ML) techniques in the context of vehicular networking is relatively recent concepts. In [
34], the authors vouched for combining AI and blockchain to realise a mobile-edge-platooning cloud platform. Advanced ML techniques, such as deep reinforcement learning (DRL) and long short-term memory (LSTM), have been successfully applied in [
35] and [
36], respectively. Although advanced AI/ ML techniques yield better results, the trade-off is the complexity, implementation time and real-time operation. In our work, we used a simple feedforward ANN-based multilayer perceptron instead and showed that considerable accuracy could also be achieved with such an introductory model.