Adaptive Virtual Resource Allocation in 5G Network Slicing Using Constrained Markov Decision Process

Network virtualization technology is generally envisaged as a promising technology to consequently satisfy various types of service requirements. On the other hand, non-orthogonal multiple access (NOMA) technology has the potential to significantly increase the spectral efficiency of the system. However, previous works that jointly address these two issues have not considered the dynamic resource allocation issue in this context. In this paper, we propose a slice-based virtual resources scheduling scheme with NOMA technology to enhance the quality-of-service (QoS) of the system. We formulate the power granularity allocation and subcarrier allocation strategies into a constrained Markov decision process problem, aiming at the maximization of the total user rate. In order to further avoid the curse of dimensionality and the expectation calculation in the optimal value function, we develop an adaptive resource allocation algorithm based on approximate dynamic programming to solve the problem. Extensive simulation works have been conducted under various system settings, and the results demonstrate that the proposed algorithm can significantly reduce the outage probability and increase the user data rate.