Dynamic Proximity-aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications (1609.03717v1)

Abstract: In this paper, a novel proximity and load-aware resource allocation for vehicle-to-vehicle (V2V) communication is proposed. The proposed approach exploits the spatio-temporal traffic patterns, in terms of load and vehicles' physical proximity, to minimize the total network cost which captures the tradeoffs between load (i.e., service delay) and successful transmissions while satisfying vehicles's quality-of-service (QoS) requirements. To solve the optimization problem under slowly varying channel information, it is decoupled the problem into two interrelated subproblems. First, a dynamic clustering mechanism is proposed to group vehicles in zones based on their traffic patterns and proximity information. Second, a matching game is proposed to allocate resources for each V2V pair within each zone. The problem is cast as many-to-one matching game in which V2V pairs and resource blocks (RBs) rank one another in order to minimize their service delay. The proposed game is shown to belong to the class of matching games with externalities. To solve this game, a distributed algorithm is proposed using which V2V pairs and RBs interact to reach a stable matching. Simulation results for a Manhattan model shown that the proposed scheme yields a higher percentage of V2V pairs satisfying QoS as well as significant gain in terms of the signal-to-interference-plus-noise ratio (SINR) as compared to a state-of-art resource allocation baseline.

• The paper proposes a dynamic proximity-aware resource allocation method for V2V communication using a two-step approach: vehicle clustering based on traffic patterns and proximity, and a many-to-one matching game for resource assignment.
• Simulations demonstrate that this method significantly enhances the percentage of V2V pairs meeting QoS requirements and improves SINR distribution compared to existing resource allocation techniques.
• The research provides a distributed mechanism integrating matching theory for robust and adaptable resource management, crucial for advancing V2V network performance in intelligent transportation systems.

Dynamic Proximity-aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications

The paper presents a sophisticated approach to resource allocation in Vehicle-to-Vehicle (V2V) communications, addressing the critical challenges posed by the dynamic and high-density environments inherent to Intelligent Transportation Systems (ITS). The authors propose a novel method that leverages the spatio-temporal traffic patterns and vehicle proximity data to optimize network cost, balancing service delay and successful transmission rates while meeting stringent Quality of Service (QoS) requirements.

The core of the proposed solution lies in a two-step process. Initially, a dynamic clustering mechanism groups vehicles into zones based on their traffic patterns and proximity. These zones help manage resources flexibly and address the challenges associated with the rapidly changing vehicular environment. Subsequently, a many-to-one matching game is utilized to assign resources within each zone. This matching game optimizes resource blocks by allowing V2V pairs and resource blocks to rank each other based on preferences aimed at minimizing service delay. Notably, this problem is shown to belong to the class of matching games with externalities, reflecting the real-time dependency between vehicular interactions and network resource availability.

The authors introduce a distributed algorithm that facilitates the dynamic interaction between V2V pairs and resource blocks, leading to a stable matching as resources are allocated. This self-organizing algorithm is crucial for minimizing control overhead and ensuring efficient utilization of network resources in a decentralized manner.

The simulation results, conducted using a Manhattan grid model, emphasize the efficacy of the proposed method. The research showcases a significant enhancement in the percentage of V2V pairs satisfying QoS requirements and marks notable improvements in SINR compared to existing resource allocation approaches. In particular, the dynamic proximity-aware scheme reduces the number of outages and improves the SINR distribution across different network sizes.

From a theoretical perspective, this work contributes to the development of distributed mechanisms for V2V communications by integrating principles from matching theory, especially those concerning matching games with externalities. Such integration not only augments the robustness of network resource management but also highlights potential avenues for further research into adaptable, real-time resource allocation frameworks.

Looking forward, this research delineates implications for advancing the practical deployment of V2V communication networks. Efficient resource management, as reported, is crucial for enabling applications such as autonomous driving and real-time traffic safety. The proposed mechanism's adaptability to fast-changing traffic conditions demonstrates significant potential for future ITS applications, potentially paving the way for enhanced vehicular networks with heightened performance and reliability.

In conclusion, while this paper addresses a pertinent challenge in V2V communications with demonstrable success, opportunities exist to explore scalability with more complex vehicular scenarios or integration with heterogeneous network environments. Additionally, future research could investigate the potential of incorporating advanced machine learning techniques to enhance the system's predictive capability, leading to further improvements in network resource utilization.