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

By Muhammad Ikram Ashraf, Medhi Bennis, Cristina Perfecto, Walid Saad. 2016

80 citations recorded by the source corpus.

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' quality-of-service (QoS) requirements. To solve the optimization problem under slowly varying channel information, it is decoupled the problem into two interrelated sub-problems. 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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, background): “In [18], the tradeoff between service delay and transmission success in V2V communications is optimized.”
Deep Reinforcement Learning Based Resource Allocation for V2V Communications (2018, methodology): “The other method is developed in [15], where vehicles are first”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, background): “Remarkably, the majority of the existing V2V literature which address latency and reliability, focus only on average performance metrics [7]–[11], which is not sufficient to enable URLLC.”