In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the number of slices controlled by a base station fluctuates in terms of user ingress and egress from the base station coverage area and service switching on the respective sets of user equipment. Therefore, when resource allocation depends on the number of slices, resources cannot be allocated when the number of slices changes. We consider a method that makes optimal-resource allocation independent of the number of slices. Resource allocation is optimized using DRL, which learns the best action for a state through trial and error. To achieve independence from the number of slices, we show a design for a model that manages resources on a one-slice-by-one-agent basis using ApeX , which is a DRL method. In ApeX , because agents can be employed in parallel, models that learn various environments can be generated through trial and error of multiple environments. In addition, we design a model that satisfies the slicing requirements without overallocating resources. Based on this design, it is possible to optimally allocate resources independently of the number of slices by changing the number of agents. In the evaluation, we test multiple scenarios and show that the mean satisfaction of the slice requirements is approximately 97%. INDEX TERMS Deep reinforcement learning, network slicing, RAN slicing, resource management.
In this paper, we study a strategy for constructing fast and practically secure round functions that yield sufficiently small values of the maximum differential and linear probabilities p, q. We consider mnbit round functions with 2-round SPN structure for Feistel ciphers. In this strategy, we regard a linear transformation layer as an n × n matrix P over {0,1}. We describe the relationship between the matrix representation and the actual construction of the linear transformation layer. We propose a search algorithm for constructing the optimal linear transformation layer by using the matrix representation in order to minimize probabilities p, q as much possible. Furthermore, by this algorithm, we determine the optimal linear transformation layer that provides p ≤ p 5 s , q ≤ q 5 s in the case of n = 8, where ps, qs denote the maximum differential and linear probabilities of s-box.S. Tavares and H. Meijer (Eds.): SAC'98, LNCS 1556, pp.
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