Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling

TL;DR

This paper introduces an efficient resource scheduling method for dynamic LEO-B5G systems using a novel meta-critic learning approach, improving adaptability and performance in over-loaded, changing environments.

Contribution

It proposes an enhanced meta-critic learning algorithm (EMCL) with a hybrid neural network and Wolpertinger policy for resilient, efficient resource scheduling in dynamic LEO-B5G networks.

Findings

EMCL outperforms previous actor-critic and meta-learning methods.

EMCL demonstrates fast response in over-loaded, dynamic environments.

The iterative algorithm provides a useful offline benchmark.

Abstract

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments.To address them, we first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.