Machine learning-driven integration of terrestrial and non-terrestrial networks for enhanced 6G connectivity

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2024-12, Vol.255, p.110875, Article 110875
• Main Authors: Aygul, Mehmet Ali, Turkmen, Halise, Cirpan, Hakan Ali, Arslan, Huseyin
• Format: Article
• Language: English
• Subjects: 3GPP; Integrated terrestrial and non-terrestrial networks; Machine learning; Non-terrestrial networks
• ISSN: 1389-1286
• DOI: 10.1016/j.comnet.2024.110875

Non-terrestrial networks (NTN)s are essential for achieving the persistent connectivity goal of sixth-generation networks, especially in areas lacking terrestrial infrastructure. However, integrating NTNs with terrestrial networks presents several challenges. The dynamic and complex nature of NTN communication scenarios makes traditional model-based approaches for resource allocation and parameter optimization computationally intensive and often impractical. Machine learning (ML)-based solutions are critical here because they can efficiently identify patterns in dynamic, multi-dimensional data, offering enhanced performance with reduced complexity. ML algorithms are categorized based on learning style—supervised, unsupervised, and reinforcement learning—and architecture, including centralized, decentralized, and distributed ML. Each approach has advantages and limitations in different contexts, making it crucial to select the most suitable ML strategy for each specific scenario in the integration of terrestrial and non-terrestrial networks (TNTN)s. This paper reviews the integration architectures of TNTNs as outlined in the 3rd Generation Partnership Project, examines ML-based existing work, and discusses suitable ML learning styles and architectures for various TNTN scenarios. Subsequently, it delves into the capabilities and challenges of different ML approaches through a case study in a specific scenario. [Display omitted] •Discusses ML algorithms by learning styles and architectures for integrating TNTNs.•Recommends suitable ML algorithms for integrated TNTN scenarios and architectures.•Incorporates an integrated TNTNs case study that applies the discussed ML algorithms.•Reviews the use cases and scenarios for integrated TNTNs.