Parallel and Successive Resource Allocation for V2V Communications in Overlapping Clusters

TL;DR

This paper proposes two resource allocation schemes for V2V communications in overlapping clusters, aiming to improve efficiency and reduce conflicts with lower complexity than exhaustive search methods.

Contribution

It introduces bipartite graph matching-based successive and parallel allocation schemes tailored for V2V mode-3, focusing on overlapping clusters at intersections or merging highways.

Findings

BGM-SA achieves near-optimal performance in simulations.

BGM-PA offers a less complex alternative with subpar performance.

Different metrics for channel condition estimation impact the efficiency of BGM-PA.

Abstract

The 3rd Generation Partnership Project (3GPP) has introduced in Rel. 14 a novel technology referred to as vehicle--to--vehicle (V2V) \textit{mode-3}. Under this scheme, the eNodeB assists in the resource allocation process allotting sidelink subchannels to vehicles. Thereupon, vehicles transmit their signals in a broadcast manner without the intervention of the former one. eNodeBs will thereby play a determinative role in the assignment of subchannels as they can effectively manage V2V traffic and prevent allocation conflicts. The latter is a crucial aspect to be enforced in order for the signals to be received reliably by other vehicles. To this purpose, we propose two resource allocation schemes namely bipartite graph matching-based successive allocation (BGM-SA) and bipartite graph matching-based parallel allocation (BGM-PA) which are suboptimal approaches with lesser complexity than exhaustive search. Both schemes incorporate constraints to prevent allocation conflicts from emerging. In this research, we consider overlapping clusters only, which could be formed at intersections or merging highways. We show through simulations that BGM-SA can attain near-optimal performance whereas BGM-PA is subpar but less complex. Additionally, since BGM-PA is based on inter-cluster vehicle pre-grouping, we explore different metrics that could effectively portray the overall channel conditions of pre-grouped vehicles. This is of course not optimal in terms of maximizing the system capacity---since the allocation process would be based on simplified surrogate information---but it reduces the computational complexity.