Sub-Optimum Radio Resource Allocation in Vehicle-To-Vehicle Communications Based on A Multi-Step Hungarian Algorithm

The main goal of this investigation is to optimally allocate radio resources to vehicle-to-vehicle users in the widely used model of Manhattan urban intersection, where the number of requests is more than the number of available resources. We first examine the method of orthogonal resource allocation based on clustering, which is used as a basis for evaluating the performance of the proposed multi-step Hungarian algorithm. In the first proposed method, first, the available orthogonal resources are randomly allocated to automotive users, and then based on an interference-limited multi-step Hungarian algorithm, similar resources are allocated to other requests. Next, improvements are made to this algorithm so that in the first step of the proposed algorithm, orthogonal resources are allocated to the nearest neighbors' users. Finally, by clustering based on the k-means method that considers the physical location of the vehicle pairs, try to find a good trade-off between the time complexity, the number of serviced users, the required number of clusters, and signal to interference plus noise ratio (SINR). By numerical analysis, we show the superiority of the proposed method based on the multi-step Hungarian algorithm, with and without clustering, for a different number of users.