5G network slicing and drone-assisted applications: a deep reinforcement learning approach

Linh Le, Tu N. Nguyen, Kun Suo, Jing Selena He

Abstract: 5G radio access network (RAN) slicing is gaining momentum in finding applications in a wide range of domains, especially those requiring high-speed data transmissions such as space-air-ground integrated networks (i.e., drone-based systems). A key challenge in building a robust RAN slicing to support these applications is, therefore, designing a RAN slicing (RS)-configuration scheme that can utilize information such as resource availability in substrate networks as well as the interdependent relationships among slices to map (embed) virtual network functions (VNFs) onto live substrate nodes. With such motivation, we propose a machine-learning-powered RAN slicing scheme that aims to accommodate maximum numbers of slices (a set of connected VNFs) within a given request set. We present a deep reinforcement scheme that is called Deep Allocation Agent (DAA). In short, DAA utilizes an empirically designed deep neural network that observes the current states of the substrate network and the requested slices to schedule the slices of which VNFs are then mapped to substrate nodes using an optimization algorithm. DAA is trained towards the goal of maximizing the number of accommodated slices in the given set by using an explicitly designed reward function. Our experiment study shows that, on average, DAA is able to maintain a rate of successfully routed slices above 80% in a resource-limited substrate network, and about 60% in extreme conditions, i.e., the available resources are much less than the demands.