This paper presents the study of machine learning (ML) algorithms for the prediction of vehicular channels under impairments that arise in realistic implementations. Emerging vehicular applications will operate in complex settings with potentially abrupt and quick propagation changes. These features can be difficult to capture by ideal (complete) datasets. Therefore, we consider sets of variable length (incomplete) to reflect the rapidly changing vehicular environment. Our assumption is that, in challenging settings, measurements collected by devices or base stations (BSs) might be the only information available to train models. Our approach covers multiple sub-cases including: i) short sets for rapidly changing settings, and ii) large sets for stationary conditions. Measurements are subject to two additional impairments: incorrect sampling and noise. We use a validated synthetic model for vehicular channels to analyze a spectrum of impairment settings that emulates the transition from non-ideal to ideal conditions. This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. Linear regression (LR) is shown to have a better trade-off between performance and implementation issues when sets are short, oversampled, and with a high signal-to-noise ratio (SNR). A new method to improve the convergence of polynomial LR in sets close to the undersampling regime is proposed here. Results show that neural networks (NNs), particularly deep learning (DL), continuously reduce the mean square error (MSE) as the length of the set increases. They quickly outperform LR, even in sets near the undersampling condition at low SNR. The effectiveness of prediction is severely degraded when sets are undersampled or subject to low SNR. Convolutional NN (CNN) and particularly LSTM (Long Short-Term Memory) show more resilience to these impairments. One key objective of channel prediction is improving resource allocation to reduce latency and increase reliability, which are crucial metrics in applications such as autonomous vehicles. Our analysis contributes to the understanding (explainability) of how AI behaves under multiple impairments, which can also lead to the improvement of vehicular applications.

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.

This paper presents an overview of how Artificial Intelligence (AI) and edge technology have been used to improve wireless connectivity in multiple industrial Use Cases (UCs) of the EU project “Intelligent Secure Trustable Things” (InSecTT). We present a brief introduction of the InSecTT framework for crossdomain architecture design, which targets UCs assisted by reusable and/or interoperable technical Building Blocks (BBs). These BBs constitute the construction bricks containing AI and supporting components that were used to build different UCs. The framework consists of multiple stages based on the processing of UC/BB requirements (RQs). These stages include: i) collection, ii) harmonization, iii) refinement, iv) classification, v) architecture alignment, and vi) functionality modeling of RQs. The most relevant results of these stages are discussed here, with emphasis on the need for a refined granularity of technical components with common functionalities named sub-building blocks (SBBs), where collaboration and cross-domain reusability were optimized. The design process shed light on how AI and SBBs were implemented across different layers and entities of our reference architecture for the Internet-of-Things (IoT), including the interfaces used for information exchange. This detailed interface analysis is expected to reveal issues such as bottlenecks, constraints, vulnerabilities, scalability problems, security threats, etc. This will, in turn, contribute to identifying design gaps in AI-enabled IoT systems. The paper summarizes the SBBs related to wireless connectivity, including a general description, implementation issues, comparison of results, adopted interfaces, and conclusions across domains.