An O-RAN Approach to Spectrum Sharing Between Commercial 5G and Government Satellite Systems

Smith, R. L.; Freeberg, Connor; Machacek, Travis; Ramaswamy, Venkatesh

doi:10.1109/milcom52596.2021.9653140

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Additionally, the radio spectrum is a finite and highly sought-after resource; therefore, spectrum sharing aims to help resolve this issue by creating regulatory frameworks and developing wireless technologies to share spectrum bands between heterogeneous users. The authors in [55] proposed O-RAN with machine learning for 5G/XG and closed-loop feedback via sensing can reduce harmful interference between heterogeneous 5G and Low Earth Orbit (LEO) satellite communication systems.…”

Section: Satellite Ntnmentioning

confidence: 99%

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Recent Advances in Machine Learning for Network Automation in the O-RAN

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

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The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

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Section: Satellite Ntnmentioning

confidence: 99%

“…2021 [55] Investigated how O-RAN can be used for 5G/XG to mitigate interference between terrestrial and space communication systems.…”

Section: Year Ref Contributionmentioning

confidence: 99%

Recent Advances in Machine Learning for Network Automation in the O-RAN

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Atualmente, vive-se em um mundo com enorme quantidade de sinais transmitidos por diversos dispositivos interligados em redes de comunicac ¸ão sem fio e, a cada dia, ocorre um incremento no número e na variedade de sinais propagados [Huang et al 2020]. Este fenômeno também impacta as redes de comunicac ¸ão via satélite proporcionando um ambiente de radiocomunicac ¸ão com congestionamento e sujeito às interferências prejudiciais ao seu funcionamento [Smith et al 2021].…”

Section: Introduc ¸ãOunclassified

Um Método Baseado em Fingerprint de Sinais e Aprendizado de Máquina para Identificação de Estações Terrenas Interferentes

Azevedo

Vidal

Goldschmidt

2022

Anais Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD 2022)

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Redes via Satélite são essenciais no mundo e, por vezes, se apresentam como o único meio de conectar regiões de difícil acesso. Tais redes utilizam comunicação sem fio e são afetadas por sinais interferentes, o que torna relevante identificar a origem desses sinais. A principal técnica para identificar a origem de sinais interferentes é a geolocalização, que apresenta um grupo de estações terrenas suspeitas. Este trabalho propõe um método que pode reduzir o número de estações elencadas pela técnica da geolocalização, ao aplicar modelos de classificação a características de Radio Frequency Fingerprint extraídas dos sinais. O método proposto obteve acurácia superior a 98% em experimentos com dados reais envolvendo 64.800 instâncias de sinais e 6 estações terrenas.

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