Fuzzy Matching Learning for Dynamic Resource Allocation in Cellular V2X Network

Benefiting from the global deployment and fast commercialization of cellular systems, cellular-enabled vehicle-to-everything (V2X) opens up a significant prospect in the foreseeable future, for supporting proliferating vehicular services. Reliable and efficient resource allocation is a prerequisite for enjoying excellent quality of vehicular communication experiences, which yet still remains a serious challenge due to the inherent vehicle mobility and network heterogeneity. In this work, taking the rapidly varying channel state information (CSI) into consideration, we investigate the joint time-frequency allocation problem for the heterogeneous cellular V2X network, where resource sharing based on hierarchical reuse among different V2X communication types are granted. To address the high dynamics of V2X environment, a mapped fuzzy space is introduced, in which the uncertain CSI is interpreted as fuzzy numbers. This could effectively avoid the deterioration of such dynamic environment to the stability and convergence of optimization mechanisms. On this basis, we construct a two-side many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with the dynamic and uncertain information, and further propose a vehicle-resource dynamic matching algorithm to solve this MM-FMG problem. Our developed scheme can achieve both reliable and efficient resource assignment by excluding the precise CSI, which is hence more suitable to the concerned V2X scenarios. Finally, the superior performances of our designed algorithm are demonstrated via numerical simulations.