UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

TL;DR

This paper reviews recent learning algorithms for UAV-assisted space-air-ground integrated networks (SAGIN), focusing on optimization methods and real-world deployment scenarios, highlighting the superiority of 3D satisfaction-based learning algorithms.

Contribution

It provides a methodological review of recent learning algorithms for UAV planning in SAGINs, emphasizing 3D trajectory optimization and real-world configurations.

Findings

3D satisfaction-based learning outperforms other algorithms in simulations.

The review covers reward functions and state-of-the-art algorithms like Q-learning and particle swarm optimization.

Guidelines for design and deployment of UAV-assisted SAGINs are discussed.

Abstract

Recent technological advancements in space, air, and ground components have made possible a new network paradigm called space-air-ground integrated network (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.