Prototyping near-real time RIC O-RAN xApps for Flexible ML-based Spectrum Sensing

Rego, Iago; Medeiros, Lucas Oliveira de; Alves, None Pedro V. A.; Goldbarg, Mateus; Lopes, Vítor V.; Flor, Daniel; Barros, Wysterlânya K. P.; Filho, Vinicius; Sousa, Vicente A. de; Aranha, Eduardo; Martins, Allan de Medeiros; Fernandes, Marcelo A. C.; Fontes, Ramon dos Reis; Neto, Alcides de Oliveira Wanderley

doi:10.1109/nfv-sdn56302.2022.9974940

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Há também propostas que auxiliam a tomada de decisões de alocação de recursos no Near-RT RIC. Em [Rego et al, 2022] propõem-se xApps para realizar sensoriamento de espectro, que pode ser utilizado para fornecer informações para alocação e escalonamento dos recursos da O-RU.…”

Section: Alocação De Recursosunclassified

Gerenciamento e Orquestração de Serviços em O-RAN: Inteligência, Tendências e Desafios

Couto,

Mattos,

Moraes

et al. 2023

Minicursos Do XLI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

View full text Add to dashboard Cite

As redes de acesso via rádio (Radio Access Networks -RANs) atuais adotam soluções monolíticas que implementam toda a pilha de protocolos das comunicações celulares. Essa abordagem monolítica não favorece a integração de infraestruturas de diferentes fornecedores ou o desenvolvimento de ferramentas de controle e gerenciamento agnósticas às interfaces proprietárias. As redes móveis de próxima geração (Next Generation -NextG) são nativamente baseadas em nuvem e construídas sobre arquiteturas desagregadas com hardware de diversos fabricantes e inteligência incorporada. As interfaces de controle padronizadas permitem a definição de laços de controle fechados que garantem a execução de redes autônomas e auto-otimizadas. A O-RAN Alliance é um consórcio formado por membros da indústria e instituições acadêmicas, que visa prover a arquitetura das redes móveis de próxima geração, em que as operadoras de telecomunicações usam interfaces padronizadas e abertas para controlar infraestruturas de diferentes fornecedores e entregar serviços de alto desempenho para os assinantes. O consórcio propõe uma arquitetura inovadora baseada em dois princípios básicos: (i) as funcionalidades da estação rádio-base (Radio Base Station -RBS) são virtualizadas como funções de rede e divididas em vários nós de rede, unidade central (O-RAN Central Unit -O-CU), unidade distribuída (O-RAN Distributed Unit -O-DU) e unidade de rádio (O-RAN Radio Unit -O-RU); e (ii) a existência de um controlador inteligente da rede de acesso via rádio (RAN Intelligent Controller -RIC), que fornece uma abstração centralizada da rede, permitindo que as operadoras implantem funções personalizadas do plano de controle. O consórcio O-RAN prevê laços de controle fechado operando em diferentes escalas de tempo, dependendo das ações de controle, gerenciamento e orquestração a serem implantadas na rede. O objetivo deste capítulo é apresentar a arquitetura O-RAN, com o foco na inteligência para o gerenciamento e a orquestração de serviços.

show abstract

Section: Alocação De Recursosunclassified

Gerenciamento e Orquestração de Serviços em O-RAN: Inteligência, Tendências e Desafios

Couto,

Mattos,

Moraes

et al. 2023

Minicursos Do XLI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

View full text Add to dashboard Cite

show abstract

“…For many vendors, operators, policy makers, and telecom ecosystem stakeholders, the O-RAN movement is crucial to the future of the 3GPP umbrella system, including 4G, 5G, and Wi-Fi [2]. These kinds of O-RAN stakeholders welcome the potential of this openness initiative to improve competition, network lexibility, cost effectiveness, and features through a centralized Radio Access Network (RAN) Intelligent Controller (RIC) and data-driven closed-loop control [3].…”

Section: Introductionmentioning

confidence: 99%

Open-source emulation-based test environment to settle O-RAN-compliant trials

Fontes¹,

Martins²,

Sousa³

et al. 2023

ITU J-FET

View full text Add to dashboard Cite

Experimental tools are a key factor in both academic and industrial research communities to create design evaluations of new networking technologies that involve troubleshooting or changing the planning of deployed networks. Physical Software-Defined Radio (SDR) experimental platforms enable a design solution for the quick prototyping of wireless communication systems. However, SDR-based experimental platforms incur high costs, which leads to scalability limitations in the experimental settings. Having said this, network simulators, emulators, and new testbeds have attracted increasing attention. Emulation-based research prototyping can be distinguished from real communication networks and SDR-based platforms by allowing a tradeoff between cost and flexibility. This paper examines the Mininet-RAN emulation tool, which, as well as Radio Access Network (RAN) modeling, provides a way to test Open RAN Intelligent Controller (RIC) services without the need to deploy an entire RAN infrastructure. The Mininet-RAN creates virtual network elements, such as hosts, L2/L3 devices, controllers, and links, by combining some of the best emulator features, hardware testbeds, and simulators. By running the current code of standard practice Unix/Linux network applications and network stack, the Mininet-RAN enables real-world network data traffic patterns to be delivered to the RIC, regarding the most significant aspect of the dynamic generation of wireless system's KPIs. We provide the basic code of Mininet-RAN for the first two O-RAN Alliance-defined use cases involving V2X and UAV. The xApps are being implemented in O-RAN SC near-RT RIC, with Mininet-RAN which provides a closed-loop validation environment.

show abstract

“…Finally, the O-RAN architecture defined by O-RAN Alliance permits us to enhance the traditional RAN functions with AI via the introduction of the RAN Intelligent Controller (RIC) platform, implementing RAN monitoring and control techniques in the form of rApps and xApps, respectively for "Non-RT (Real-Time) RIC" and "Near-RT RIC". Suitable uses for the RIC are mainly focused on AI-enabled RAN optimization, but can also include advanced functionalities, such as the integration of RIC with MEC enabling cross-layer application design (e.g., network and QoS aware adaptive MEC applications), or again xApps for Flexible ML-based Spectrum Sensing, e.g., to enable cognitive radio concepts[26]. ML training and inference within the 5GC via NWDAF for network automation purposes, for their Release 18 it is notable how 3GPP is also working to provide intelligent transmission support for application AI/ML-based services, e.g., AI/ML model distribution, transfer, training for various applications, video/speech recognition, robot control, automotive, etc.…”

mentioning

confidence: 99%

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

Sabella¹,

Micheli

Nardini

2023

JSAN

View full text Add to dashboard Cite

The evolution of communication systems always follows data traffic evolution and further influences innovations that are unlocking new markets and services. While 5G deployment is still ongoing in various countries, data-driven considerations (extracted from forecasts at the macroscopic level, detailed analysis of live network traffic patterns, and specific measures from terminals) can conveniently feed insights suitable for many purposes (B2B e.g., operator planning and network management; plus also B2C e.g., smarter applications and AI-aided services) in the view of future 6G systems. Moreover, technology trends from standards and research projects (such as Hexa-X) are moving with industry efforts on this evolution. This paper shows the importance of data-driven insights, by first exploring network evolution across the years from a data point of view, and then by using global traffic forecasts complemented by data traffic extractions from a live 5G operator network (statistical network counters and measures from terminals) to draw some considerations on the possible evolution toward 6G. It finally presents a concrete case study showing how data collected from the live network can be exploited to help the design of AI operations and feed QoS predictions.

show abstract

Prototyping near-real time RIC O-RAN xApps for Flexible ML-based Spectrum Sensing

Cited by 5 publications

References 10 publications

Gerenciamento e Orquestração de Serviços em O-RAN: Inteligência, Tendências e Desafios

Gerenciamento e Orquestração de Serviços em O-RAN: Inteligência, Tendências e Desafios

Open-source emulation-based test environment to settle O-RAN-compliant trials

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

Contact Info

Product

Resources

About