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

arXiv: https://arxiv.org/abs/2110.06787 · HTML: https://mlsanity.com/paper/2110.06787/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Meta-Learning Representations for Continual Learning](https://mlsanity.com/paper/1905.12588/) (2019)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Learning to Learn: Meta-Critic Networks for Sample Efficient Learning](https://mlsanity.com/paper/1706.09529/) (2017)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels](https://mlsanity.com/paper/1910.09945/) (2019)
- [Improvement of the Global Connectivity using Integrated Satellite-Airborne-Terrestrial Networks with Resource Optimization](https://mlsanity.com/paper/1912.03828/) (2019)
- [Massive MIMO Transmission for LEO Satellite Communications](https://mlsanity.com/paper/2002.08148/) (2020)
- [Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment](https://mlsanity.com/paper/2011.07782/) (2020)
- [Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks](https://mlsanity.com/paper/2005.12521/) (2020)
- [Iteration Complexity Analysis of Multi-Block ADMM for a Family of Convex Minimization without Strong Convexity](https://mlsanity.com/paper/1504.03087/) (2015)
- [Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization](https://mlsanity.com/paper/2006.13610/) (2020)
- [Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks](https://mlsanity.com/paper/1906.07233/) (2019)
- [Ultra-dense LEO: Integrating Terrestrial-Satellite Networks into 5G and Beyond for Data Offloading](https://mlsanity.com/paper/1811.05101/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)
- [Trends, Advancements and Challenges in Intelligent Optimization in Satellite Communication](https://mlsanity.com/paper/2410.03674/) (2024)
- [Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks](https://mlsanity.com/paper/2411.08896/) (2024)

## Abstract (arXiv)

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.