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In this paper, a novel proximity and load-aware resource allocation forvehicle-to-vehicle (V2V) communication is proposed. The proposed approachexploits the spatio-temporal traffic patterns, in terms of load and vehicles'physical proximity, to minimize the total network cost which captures thetradeoffs between load (i.e., service delay) and successful transmissions whilesatisfying vehicles's quality-of-service (QoS) requirements. To solve theoptimization problem under slowly varying channel information, it is decoupledthe problem into two interrelated subproblems. First, a dynamic clusteringmechanism is proposed to group vehicles in zones based on their trafficpatterns and proximity information. Second, a matching game is proposed toallocate resources for each V2V pair within each zone. The problem is cast asmany-to-one matching game in which V2V pairs and resource blocks (RBs) rank oneanother in order to minimize their service delay. The proposed game is shown tobelong to the class of matching games with externalities. To solve this game, adistributed algorithm is proposed using which V2V pairs and RBs interact toreach a stable matching. Simulation results for a Manhattan model shown thatthe proposed scheme yields a higher percentage of V2V pairs satisfying QoS aswell as significant gain in terms of the signal-to-interference-plus-noiseratio (SINR) as compared to a state-of-art resource allocation baseline.

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