| Summary: | With the development of 5G technology and Internet of Things (IoT), more and more devices are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature of 5G, enables a flexible bandwidth resource allocation policy, and facilitates various types of services to operate on different network slices. However, RAN slicing resources is scarce, thus effective management of wireless bandwidth resources in RAN slicing becomes indispensable to improve user satisfaction. Extensive research has investigated into RAN slicing, but they do not take user mobility into consideration. While RAN slicing allocation has a great impact on user experience in mobile 5G scenarios, user mobility poses great challenges to network management and causing unsatisfaction of users. In this paper, we propose a new RAN slicing allocation strategy based on machine learning, to maximize spectrum efficiency while guaranteeing the Service Satisfaction Ratio (SSR) of various slicing services. To further alleviate the SSR fluctuation brought by user mobility, we study into the temporal characteristics of user mobility and preprocess the state sequences using Long Short-Term Memory (LSTM) networks. Finally, these sequences are taken as the input of an Advantage Actor Critic (A2C) reinforcement learning network to develop a RAN slicing allocation policy. We conduct comprehensive simulations, and the results show that the performance of the proposed mechanism outperforms the traditional mechanism in ensuring SSR and enhancing the spectrum efficiency.
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