Graph-Based Resource Allocation with Conflict Avoidance for V2V Broadcast Communications

TL;DR

This paper introduces a graph-based resource allocation method for V2V broadcast communications that optimizes spectrum use and minimizes interference by leveraging vehicle positions and spectrum data, ensuring conflict-free transmissions.

Contribution

The paper proposes a novel bipartite graph approach for resource allocation in V2V communications that guarantees optimal, conflict-free, and efficient spectrum sharing under high vehicle density scenarios.

Findings

The method achieves optimal resource allocation mathematically and through simulations.

It outperforms existing approaches, especially in dense vehicular environments.

The approach maintains low computational complexity comparable to traditional graph matching.

Abstract

In this paper we present a graph-based resource allocation scheme for sidelink broadcast vehicle-to-vehicle (V2V) communications. Harnessing available information on the geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to several different vehicles in order for them to broadcast their signals. Hence, vehicles sharing the same resources would ideally be in different communications clusters for the interference level-generated due to resource repurposing-to be maintained under control. Within a communications cluster, it is crucial that vehicles transmit in orthogonal time resources to prevent conflicts as vehicles-with half-duplex radio interfaces--cannot transmit and receive simultaneously. In this research, we have envisaged a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the signal-to-interference-plus-noise ratio (SINR) that vehicles perceive. The aforementioned constraint on time orthogonality of allocated resources can be approached by aggregating conflicting vertices into macro-vertices which, in addition, narrows the search space yielding a solution with computational complexity equivalent to the conventional graph matching problem. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations showing that the proposed method outperforms other competing approaches, specially in scenarios with high vehicular density.