Due to the sensitivity of the information exchanged in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication, generating secret keys is critical to secure these communications. As nature is open access, distributed keys are more vulnerable to attacks in the vehicular environment. Physical layer key generation methods using wireless channel characteristics show promise in preventing such attacks, generating keys independently, and removing the need for distribution. In this paper, we present a novel key generation approach in a real vehicular environment based on Channel State Information (CSI), including a new algorithm for key bit extraction. We implemented our algorithm using USRP B210 Software-Defined Radios (SDR) and the industry-standard V2X communication protocol: IEEE 802.11p. The proposed key generation protocol uses the CSI values of each sub-carrier as a source of randomness, from which bits are extracted using a new QAM demodulator quantizer (QAM-Dem-Quan). We compared our technique to state-of-the-art Received Signal Strength (RSS)-based approaches, and show that our method achieves better performance. Moreover, we reached a min-entropy of approximately 70% for the generated keys and a key generation rate of less than $150~\mu \text{s}$ /key for key lengths ranging from 16 to 128 bits.

This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy ($96.48\%$). Our model is compared to related approaches and state-of-the-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.

End-to-end wireless communication is new concept expected to be widely used in the physical layer of future wireless communication systems (6G). It involves the substitution of transmitter and receiver block components with a deep neural network (DNN), aiming to enhance the efficiency of data transmission. This will ensure the transition of autonomous vehicles (AVs) from self-autonomy to full collaborative autonomy, that requires vehicular connectivity with high data throughput and minimal latency. In this article, we propose a novel neural network receiver based on transformer architecture, named TransRx, designed for vehicle-to-network (V2N) communications. The TransRx system replaces conventional receiver block components in traditional communication setups. We evaluated our proposed system across various scenarios using different parameter sets and velocities ranging from 0 to 120 km/h over Urban Macro-cell (UMa) channels as defined by 3GPP. The results demonstrate that TransRx outperforms the state-of-the-art systems, achieving a 3.5dB improvement in convergence to low Bit Error Rate (BER) compared to convolutional neural network (CNN)-based neural receivers, and an 8dB improvement compared to traditional baseline receiver configurations. Furthermore, our proposed system exhibits robust generalization capabilities, making it suitable for deployment in large-scale environments.

Les véhicules connectés autonomes devraient être largement déployés dans le cadrede la prochaine génération de systèmes de transport et de la future route intelligente.Comme l'environnement véhiculaire est très mobile, les messages transmis sont affectés par l'effet de canal sans fil. L'estimation de canal devient ainsi l'une des tâchesles plus difficiles des communications véhiculaires (V2X). Dans ce contexte, nousproposons un nouvel algorithme d'estimation de canal MSCR (Multiple SequentialConstraint Removal) dédiée aux communications V2X basées sur les systèmes OFDM.De plus, l'identification de l'environnement dans lequel le véhicule circule est assez importantepour permettre au véhicule de prendre les bonnes décisions de conduite autonome.Ainsi, en exploitant les caractéristiques estimées du canal sans fil, nous proposonsune nouvelle approche d'identification de l'environnement véhiculaire basée surle Deep Learning, où les CSI estimées sont utilisées comme caractéristiques d'entréepour le modèle proposé. En outre, comme l'environnement véhiculaire est un accès ouvert,les messages échangés via la communication V2X sont vulnérables aux attaques.Par consequent, nous proposons un nouvel algorithme de generation de clé à base dela couche physique afin de sécuriser les communications véhiculaires, où les valeursCSI sont utilisées comme source aléatoire. Notre algorithme de génération de clés a étéimplémenté sur des cartes SDR USRP et a été testé sur un environnement véhiculaireréel. Les résultats de l'évaluation des performances de toutes les méthodes proposées(algorithme d'estimation de canal MSCR, approche d'identification d'environnementet algorithme de génération de clés), montrent qu'elles répondent parfaitement auxexigences requises dans un environnement véhiculaire.

Neural receiver models are proposed to jointly optimize multiple functionalities of wireless receivers; however, a comprehensive receiver model that replaces the entire physical layer blocks has not yet been presented in the literature. In this work, we introduce a novel hybrid neural receiver (H-NR) built on Transformer encoder blocks and Graph Neural Network (GNN), as part of an end-to-end wireless communication framework. In our communication framework, we assume vehicle to network (V2N) uplink scenario where information is transmitted by vehicle and received at the base station (BS). Our proposed H-NR model replace OFDM resource grid demapping, channel estimation, signal equalization, demodulation, and channel decoding. To test the adaptability of our proposed model on unseen conditions, we evaluate its performance for various scenarios, including a vehicle speed of range [0-60] km/h, a carrier frequency of 5.9GHz, and a cluster delay line (CDL) channel model. Furthermore, we assess the performance of our proposed H-NR on multimodal data, such as images, audio, GPS, radar, and LiDAR, to examine its adaptability in real-world use cases. The simulation results clearly demonstrate that our proposed model outperforms the state-of-the-art neural receiver by approximately 0.5 dB in terms of reconstruction and error correction.