Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing

TL;DR

This paper introduces a dynamic resource allocation scheme for 5G network slicing that optimizes uplink transmission rates and QoS through a novel Markov decision process approach with online learning.

Contribution

It proposes a joint optimization framework for subchannel and power control in RAN slicing using an approximate dynamic programming method with online stochastic learning.

Findings

The algorithm effectively meets delay constraints.

It improves user transmission rates over baseline schemes.

The approach addresses the curse-of-dimensionality in optimal control.

Abstract

The fifth generation and beyond wireless communication will support vastly heterogeneous services and use demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient technology to meet these diverse demands. In this paper, we propose a dynamic virtual resources allocation scheme based on the radio access network (RAN) slicing for uplink communications to ensure the quality-of-service (QoS). To maximum the weighted-sum transmission rate performance under delay constraint, formulate a joint optimization problem of subchannel allocation and power control as an infinite-horizon average-reward constrained Markov decision process (CMDP) problem. Based on the equivalent Bellman equation, the optimal control policy is first derived by the value iteration algorithm. However, the optimal policy suffers from the widely known curse-of-dimensionality problem. To address this problem, the linear value function approximation (approximate dynamic programming) is adopted. Then, the subchannel allocation Q-factor is decomposed into the per-slice Q-factor. Furthermore, the Q-factor and Lagrangian multipliers are updated by the use of an online stochastic learning algorithm. Finally, simulation results reveal that the proposed algorithm can meet the delay requirements and improve the user transmission rate compared with baseline schemes.