# 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. network slicing; radio access network; resource allocation; case-based reasoning; k-nearest neighbors +
Footnote †: journal: _Sci._  
heuristics, best efforts, and random methods. [PERSON] et al. [6] reserve a portion of the radio resource in the BS, and then utilize DRL to change the resources in each slice autonomously in terms of resource consumption and satisfaction of the slices. [PERSON] et al. [7] proposed a negotiated spectrum resource sharing agreement between operators and infrastructure providers to improve spectral efficiency. To allocate resources across eMBB and URLLC slices, [PERSON] et al. [8] proposed a two-timescale technique based on the Lyapunov optimization. It performed similarly to the exhaustive searching algorithm (the optimal solution). To achieve a trade-off between slice performances, [PERSON] et al. [9] employed the Stackelberg game to distribute subchannels for local radio resource managers (LRRMs) within slices. They analyzed the service performance of power consumption and user satisfaction. The low-complexity methods perform similarly to exhaustive search and greatly outperform other benchmarks. [PERSON] et al. [10] proposed using a game theoretic approach to allocate resource. [PERSON] et al. [11] proposed using a genetic algorithm (GA) to determine the most suitable slice boundary to achieve the optimal inter-slice resource management. To deal with co-channel interference, [PERSON] et al. [12] proposed a connection admission control (CAC) mechanism to achieve effective isolation for network slicing. In conclusion, most optimization algorithms have not maximized the QUR in the case of a limited spectrum, and only obtained a performance similar to an exhaustive search. We research the slice bandwidth ratio prediction based on a case library, which stores a massive number of cases by an exhaustive search method that can find the optimal solution to maximize the QUR. It can realize very low prediction errors and run costs. In summary, the key contributions of this paper are summarized as follows:  
* To reduce the computational complexity and determine the optimal slice ratio (the proportion of bandwidth occupied by slices), we built a case library to store the user distribution scenario and the optimal slice ratio produced by exhaustive searching. * We have considered the QUR, which is to optimize the slice service. * The CBR framework is proposed to form a complete system. The predicted error is reduced by revising and retaining consistency. * The KNN algorithm is proposed to determine the optimal slice bandwidth ratio for the new case based on the database. To reduce the prediction error and run cost, the sparsity reduction method, which joined the least square, spare learning, and locality-preserving projection, is utilized to optimize the \(k\) value. We defined it as optimizing KNN (O-KNN). The rest of the paper is structured as follows. Section 2 presents the system model and details the related components of the system architecture. Section 3 presents related intelligent techniques required to build the proposed approach.