Towards Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication (1704.06894v2)

Abstract: Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling technology to ensure traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation for V2V communication is proposed. The proposed approach exploits the spatial-temporal aspects of vehicles in terms of their physical proximity and traffic demands, to minimize the total transmission power while considering queuing latency and reliability. Due to the overhead caused by frequent information exchange between vehicles and the roadside unit (RSU), the centralized problem is decoupled into two interrelated subproblems. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles in zones based on their physical proximity. Given the vehicles' traffic demands and their QoS requirements, resource blocks are assigned to each zone. Second, leveraging techniques from Lyapunov stochastic optimization, a power minimization solution is proposed for each V2V pair within each zone. Simulation results for a Manhattan model have shown that the proposed scheme outperforms the baseline in terms of average queuing latency reduction up to 97% and significant improvement in reliability.

• The paper proposes a semi-centralized resource allocation framework using RSU-assisted clustering and Lyapunov optimization to achieve low latency and high reliability in V2V communication.
• The approach efficiently partitions the problem, grouping vehicles by proximity to manage interference and implementing localized power control.
• Simulation results show the framework dramatically reduces queuing latency by up to 97% and significantly enhances reliability compared to existing methods.

Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication: Resource Allocation Strategies

The paper "Towards Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication" presents an innovative approach to addressing the challenges of resource allocation in Vehicle-to-Vehicle (V2V) communication systems. This paper proposes a proximity and QoS-aware resource allocation framework designed to optimize transmission power while meeting stringent queuing latency and reliability requirements.

The central focus of the paper is the development of a novel resource allocation scheme that takes into account spatial and temporal aspects of vehicular networks. The authors have efficiently partitioned the centralized problem into two distinct subproblems, alleviating the overhead associated with high-frequency information exchange between vehicles and roadside units (RSUs). The first subproblem introduces RSU-assisted virtual clustering, grouping vehicles based on physical proximity to alleviate interference and reduce transmission power demands. The second subproblem utilizes Lyapunov stochastic optimization to achieve power minimization within these zones, ensuring queuing latency and reliability constraints are upheld. Simulation results in a Manhattan mobility model demonstrate that this approach significantly outperforms existing baseline models, achieving up to a 97% reduction in average queuing latency and substantial improvements in transmission reliability.

Radio Resource Management (RRM) for V2V communication faces challenges due to the stringent QoS requirements. The proposed solutions leverage both queue length constraints and geographical information, aiming to balance power consumption with queuing latency. By employing Markov's inequality, the paper transforms probabilistic queue length constraints into latency requirements, providing a clear and computationally efficient method for ensuring reliability.

Notably, the paper implements a semi-centralized approach—pairing RSU-mediated cluster formation for RB allocation with localized power control managed at the VUE level. This clever division mitigates interference and enhances communication efficiency without requiring continuous updates from transmitting vehicles. The zone formation is proximity-driven, focusing on minimizing interference and maximizing throughput for the allocated resources. Once formed, zones receive RB allocations proportional to the VUE demand, further optimizing resource utilization.

Numerical results underscore the effectiveness of the proposed framework across varying densities of VUE pairs. The tradeoff analysis between power consumption and queuing latency highlights the scalability and robustness of the approach, affirming its applicability in real-world densely populated vehicular networks. Additionally, reliability metrics indicate substantial performance enhancements over existing models, emphasizing the feasibility of achieving ultra-reliable low-latency communication in V2V systems.

Implications of this research extend to both theoretical and practical domains. The innovative clustering and allocation mechanisms offer a paradigm shift in handling V2V communication complexities, potentially informing future models for intelligent transportation systems. By adhering to stringent QoS criteria and providing a scalable and adaptable solution, this work paves the way for advancements in autonomous vehicular technologies and related mission-critical applications.

In summary, the paper exemplifies an advanced resource allocation strategy for V2V communication systems, characterized by its dual focus on minimizing power consumption and ensuring reliability. Through meticulous simulation and analysis, the authors establish the efficacy of their approach in meeting contemporary and future demands for ultra-reliable low-latency vehicular communication networks.