Distributed and Scalable Radio Resource Management for mmWave V2V Relays towards Safe Automated Driving

The millimeter-wave (mmWave) Vehicle-to-Vehicle (V2V) communication system has drawn attention as a critical technology to extend the restricted perception of onboard sensors and upgrade the level of vehicular safety that requires a high data rate. However, co-channel inter-link interference presents significant challenges for scalable V2V communications. To overcome such limitations, this paper firstly analyzes the required data rate ensuring maneuver safety via mmWave V2V relays in an overtaking traffic scenario. Based on these preparations, we propose a distributed radio resource management scheme that integrates spatial, frequency, and power domains for two transmission ranges (short/long). In the spatial domain, ZigZag antenna configuration is utilized to mitigate the interference, which plays a decisive role in the short inter-vehicle distance. In frequency and power domains, two resource blocks are allocated alternately, and transmit power is controlled to suppress the interference, which has a decisive impact on interference mitigation in the long inter-vehicle distance. Simulation results reveal that the achievable End-to-End (E2E) throughput maintains consistently higher than the required data rate for all vehicles. Most importantly, it works effectively in scalable mmWave V2V topology.

Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

Background: The exponential increase in pattern of vehicles on the roads demands a need to develop a vehicular infrastructure that may not only ease congestions and provide a better experience but also pivot the levels of safety among users. The development of wireless technology has made it convenient for machines, devices and vehicles to interact with one another. The efficacy of this wireless communications relies on utilising current and available technology to enable information to be shared efficiently. In the wake of the available advancement in wireless technology, a new dynamic spectrum management (DSM) in vehicle-to-vehicle (V2V) communication that coexists with the existing Long-Term Evolution (LTE) network to increase the throughput in V2V communication is proposed. This will provide some solutions to enable a more efficient vehicular infrastructure. Methods: This paper focuses on the utilization of DSM in V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles. Further investigations are done to identify the effect of interference in the dynamic spectrum by observing the path loss, SINR, and the throughput with various interfering users. Results: The results show that the performance of the proposed DSM augments a significant improvement in the overall throughput and the signal-to-interference-plus-noise ratio (SINR) value is reduced by up to 60% when compared to the fixed spectrum allocation. Conclusions: Although the dynamic spectrum is still affected by the interference from the existing cellular users, the throughput of the dynamic spectrum remains sufficient to transmit the information to other vehicles.

Named Data Networking-Based On-Demand Secure Vehicle-To-Vehicle Communications

The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well-organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information-centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in-network caching and named-based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain-based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC-caching) mechanism for the placement of on-demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster-based V2V communications in the VNDN environment. The SVC-caching strategy is simulated in the NS-2 simulator. The results are evaluated based on one-hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.

Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle

Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>