Resource Allocation for Network Slicing in RAN Using Case-Based Reasoning

Abstract

As a key technology of 5G, network slicing can meet the diverse needs of users. In this research, we study network slicing resource allocation in radio access networks (RAN) by case-based reasoning (CBR). We treat the user distribution scenario as a case and stored a massive number of cases in the library. CBR is used to match a new case with cases in the case library to find similar cases and determine the best slice bandwidth ratio of the new case based on these similar cases. In the matching process, the k-nearest neighbors (KNN) algorithm is used to retrieve similar cases, the nearest k neighbors being determined by considering sparsity reduction and locality-preserving projections. Although only an initial study, the results confirm that the proposed architecture is capable of allocating resources efficiently in terms of prediction error and computational cost.