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SAMURAI: smart 5G core and multiran integration

Grant number: 20/05127-2
Support Opportunities:Research Projects - Thematic Grants
Duration: January 01, 2022 - December 31, 2026
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Convênio/Acordo: MCTI/MC
Principal Investigator:Aldebaro Barreto da Rocha Klautau Junior
Grantee:Aldebaro Barreto da Rocha Klautau Junior
Host Institution: Instituto de Tecnologia. Universidade Federal do Pará (UFPA). Ministério da Educação (Brasil). Belém , SP, Brazil
Pesquisadores principais:
Cristiano Bonato Both ; José Ferreira de Rezende ; Kleber Vieira Cardoso ; Luciano Leonel Mendes
Associated researchers: Anderson Reis Rufino Marins ; Antonio Carlos de Oliveira Júnior ; Ciro José Almeida Macedo ; Cleverson Veloso Nahum ; Davi da Silva Brilhante ; Elton Vivot Dias ; Felipe Hauschild Grings ; Gabriel Matheus Faria de Almeida ; Gustavo Dias de Oliveira ; Gustavo Zanatta Bruno ; Hudson de Paula Romualdo ; Ilan Sousa Correa ; Joanna Carolina Manjarres Meneses ; Kleber Vieira Cardoso ; Leonardo Lira Ramalho ; Luan Assis Gonçalves ; Lucas Costa Shibata ; Lucio Rene Prade ; Mariana Baracat de Mello ; Pedro Henrique Carneiro de Souza ; Roberto Michio Marques Kagami ; Rogério Sousa e Silva ; SAND LUZ CORREA ; Thiago Guimarães Tavares ; Victor Hugo Lázaro Lopes
Associated scholarship(s):23/14714-7 - Integration of non-3GPP IoT wireless access network with 5G core, BP.TT
23/09197-3 - IC-REQ-UNISINOS1 scholarship [UNISINOS], BP.IC
22/14431-2 - XAI algorithms applied to long range 5G networks, BP.PD
22/14965-7 - SAMURAI - Smart 5G Core And MUltiRAn Integration, BP.TT
22/03457-0 - SAMURAI: smart 5G core and MUltiRAn integration, BP.TT


5G networks will meet the different requirements of new services and applications, such as IoT, virtual/augmented reality, autonomous cars, and precision agrobusiness. To deal with this diversity, multiple modes of operation, provided by different wireless access technologies, have been defined. In addition, 5G networks are being developed under an intense softwarization process, characterized by the use of cloud, virtualization and programmability. This process is significant in access networks and even more notable in the 5G core. Given the many challenges, there are still several open issues, such as the integration of non-3GPP IoT access network technologies to a 5G core. The SAMURAI project thus proposes to research, deploy and extend 5G systems, developing the software needed to demonstrate the integration of wireless access technologies into the 5G core. Additionally, the project will address issues related to the adoption of Artificial Intelligence/Machine Learning (AI/ML) as a critical component in the evolution of 5G networks. Although standardization institutions are advancing in defining a framework, there are still several gaps for the full use of AI/ML in 5G. To overcome some of the most relevant gaps, the SAMURAI project will determine AI/ML algorithms and techniques suitable for problems in access and core networks, such as link adaptation to channel conditions, beam selection in millimeter waves and functionality positioning, in addition to advancing the state-of-the-art in data collection and use. Bringing together academic institutions and RNP, the project enables the assessment of AI/ML in a nationwide network, allowing the development of solutions that have predictable behavior and can be effectively adopted in production systems that leverage priority use cases in Brazil, such as online education enabled by long-distance networks. (AU)

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Scientific publications (14)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
LUIZ A. M. PEREIRA; EDUARDO S. LIMA; LUCIANO L. MENDES; ARISMAR CERQUEIRA S. JR.. Machine Learning-Based Digital Pre-Distortion Scheme for RoF Systems and Experimental 5G mm-waves Fiber-Wireless Implementation. J. Microw. Optoelectron. Electromagn. Appl., v. 22, n. 1, p. 172-183, . (20/05127-2)
ALMEIDA, GABRIEL M.; PINTO, LEIZER DE L.; BOTH, CRISTIANO B.; CARDOSO, KLEBER, V. Optimal Joint Functional Split and Network Function Placement in Virtualized RAN With Splittable Flows. IEEE WIRELESS COMMUNICATIONS LETTERS, v. 11, n. 8, p. 5-pg., . (20/05127-2)
SILVEIRA, LUCAS B. D.; DE RESENDE, HENRIQUE C.; BOTH, CRISTIANO B.; MARQUEZ-BARJA, JOHANN M.; SILVESTRE, BRUNO; CARDOSO, KLEBER, V. Tutorial on communication between access networks and the 5G core. Computer Networks, v. 216, p. 15-pg., . (20/05182-3, 18/23097-3, 20/05127-2)
BOAS, EVANDRO C. VILAS C.; S. E SILVA, JEFFERSON D. S.; DE FIGUEIREDO, FELIPE A. P.; MENDES, LUCIANO L. L.; DE SOUZA, RAUSLEY A. A.. Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, v. 2022, n. 1, p. 63-pg., . (21/06946-0, 20/05127-2)
GASPAR, DANILO; MENDES, LUCIANO L.; PIMENTA, TALES C.. A review on principles, performance and complexity of linear estimation and detection techniques for MIMO systems. FRONTIERS IN COMMUNICATIONS AND NETWORKS, v. 4, p. 21-pg., . (20/05127-2)
PEREIRA, LUIZ AUGUSTO MELO; MENDES, LUCIANO LEONEL; BASTOS FILHO, CARMELO JOSE ALBANEZ; SODRE JR, ARISMAR CERQUEIRA. Amplified radio-over-fiber system linearization using recurrent neural networks. JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, v. 15, n. 3, p. 11-pg., . (20/05127-2)
ALMEIDA, GABRIEL MATHEUS; LOPES, VICTOR H.; KLAUTAU, ALDEBARO; CARDOSO, KLEBER V.; IEEE. Deep reinforcement learning for joint functional split and network function placement in vRAN. 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), v. N/A, p. 6-pg., . (20/05127-2)
MELLO, MARIANA B.; MENDES, LUCIANO L.. Low-Complexity Detection Algorithms Applied to FTN-GFDM Systems. IEEE ACCESS, v. 10, p. 14-pg., . (20/05127-2)
DE SOUZA, PEDRO H. C.; MENDES, LUCIANO L.. Low-complexity deep unfolded neural network receiver for MIMO systems based on the probability data association detector. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, v. 2022, n. 1, p. 19-pg., . (20/05127-2)
PEREIRA, LUIZ A. M.; MENDES, LUCIANO L.; BASTOS-FILHO, CARMELO J. A.; CERQUEIRA S, ARISMAR. Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems. IEEE Photonics Journal, v. 14, n. 6, p. 10-pg., . (20/05127-2)
DE SOUZA, PEDRO H. C.; MENDES, LUCIANO L.. Lattice Reduction Aided Probability Data Association Detector for MIMO Systems. IEEE COMMUNICATIONS LETTERS, v. 26, n. 10, p. 5-pg., . (20/05127-2)
FRAGA, LUCIANO DE S.; ALMEIDA, GABRIEL MATHEUS; CORREA, SAND; BOTH, CRISTIANO; PINTO, LEIZER; CARDOSO, KLEBER; IEEE. Efficient allocation of disaggregated RAN functions and Multi-access Edge Computing services. 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), v. N/A, p. 6-pg., . (20/05127-2)
LOPES, VICTOR HUGO L.; ALMEIDA, GABRIEL MATHEUS; KLAUTAU, ALDEBARO; CARDOSO, KLEBER; IEEE. A Coverage-Aware VNF Placement and Resource Allocation Approach for Disaggregated vRANs. 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), v. N/A, p. 6-pg., . (20/05127-2)

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