Garantias de Latência sob Incerteza: Planejamento de Fatias URLLC em Redes B5G
Resumo
Este trabalho propõe um arcabouço estocástico para o planejamento de fatias URLLC em redes B5G, fundamentado no Cálculo de Rede Estocástico (SNC). A partir da modelagem probabilística dos processos de chegada e de serviço, é derivada uma expressão analítica em forma fechada que relaciona requisitos de latência, probabilidade de violação e, o mais importante, número máximo de usuários admissíveis por célula e fatia. O modelo permite dimensionar recursos de rádio com garantias probabilísticas de QoS, evitando o superdimensionamento de abordagens determinísticas. Resultados numéricos evidenciam ganhos significativos de capacidade quando comparados aos resultados obtidos utilizando Cálculo de Rede Determinístico (DNC). O arcabouço proposto fornece subsídios relevantes para o planejamento eficiente de fatias URLLC em ambientes multicelulares heterogêneos.Referências
3GPP (2023a). NR; Base Station (BS) radio transmission and reception. Technical Specification TS 38.104, 3rd Generation Partnership Project (3GPP). Section 5.3.2: Channel bandwidth.
3GPP (2023b). NR; Physical channels and modulation. Technical Specification TS 38.211, 3rd Generation Partnership Project (3GPP).
Adamuz-Hinojosa, O. et al. (2023). A stochastic network calculus (snc)-based model for planning b5g urllc ran slices. IEEE Transactions on Wireless Communications, 22(2):1298–1312.
Banchs, A., De Veciana, G., Sciancalepore, V., and Costa-Perez, X. (2020). Resource allocation for network slicing in mobile networks. IEEE Access, 8:214696–214706.
Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V., and Costa-Pérez, X. (2020). A machine learning approach to 5g infrastructure market optimization. IEEE Transactions on Mobile Computing, 19(3):498–512.
Choi, J., Krishnan, S., and Park, J. (2024). Latency-optimal resource allocation for uav-aided leo communication. IEEE Transactions on Vehicular Technology, 73(8):12096–12108.
del Prever, P. B., D’Oro, S., Bonati, L., Polese, M., Tsampazi, M., Lehmann, H., and Melodia, T. (2025). Pacifista: Conflict evaluation and management in open ran. IEEE Transactions on Mobile Computing.
ETSI / 3GPP (2022). Etsi ts 129 536 v17.2.0 (2022-10): 5g; 5g system; network slice admission control services; stage 3 (3gpp ts 29.536 version 17.2.0 release 17). Technical Specification TS 129 536 V17.2.0, European Telecommunications Standards Institute. Accessed: 2025-01-14.
Fidler, M. and Rizk, A. (2015). A guide to the stochastic network calculus. IEEE Communications Surveys & Tutorials, 17(1):92–105.
García-Morales, J., Lucas-Estañ, M. C., and Gozalvez, J. (2019). Latency-sensitive 5g ran slicing for industry 4.0. IEEE Access, 7:143139–143159.
Guo, C., Liang, L., and Li, G. Y. (2019). Resource allocation for high-reliability low-latency vehicular communications with packet retransmission. IEEE Transactions on Vehicular Technology, 68(7):6219–6230.
Guo, T. and Suárez, A. (2019). Enabling 5g ran slicing with edf slice scheduling. IEEE Transactions on Vehicular Technology, 68(3):2865–2877.
Le Boudec, J.-Y. and Thiran, P. (2001). Network Calculus: A Theory of Deterministic Queuing Systems for the Internet. Springer.
Patra, M., Thakur, R., and Murthy, C. S. R. (2017). Improving delay and energy efficiency of vehicular networks using mobile femto access points. IEEE Transactions on Vehicular Technology, 66(2):1496–1505.
Rocha, F. G., Almeida, G. M., Cardoso, K. V., Both, C. B., and De Rezende, J. F. (2026). Optimal Resource Allocation with Delay Guarantees for Network Slicing in Disaggregated RAN. IEEE/ACM Transactions on Networking, 34:4249–4268.
Rocha, F. G. C. and Vieira, F. H. T. (2019). A channel and queue-aware scheduling for the lte downlink based on service curve and buffer overflow probability. IEEE Wireless Communications Letters, 8(3):729–732.
Tang, J., Shim, B., and Quek, T. Q. (2019). Service multiplexing and revenue maximization in sliced C-RAN incorporated with URLLC and multicast eMBB. IEEE Journal on Selected Areas in Communications, 37(4):881–895.
Zanzi, L., Sciancalepore, V., Garcia-Saavedra, A., Schotten, H. D., and Costa-Pérez, X. (2021). Laco: A latency-driven network slicing orchestration in beyond-5g networks. IEEE Transactions on Wireless Communications, 20(1):667–682.
3GPP (2023b). NR; Physical channels and modulation. Technical Specification TS 38.211, 3rd Generation Partnership Project (3GPP).
Adamuz-Hinojosa, O. et al. (2023). A stochastic network calculus (snc)-based model for planning b5g urllc ran slices. IEEE Transactions on Wireless Communications, 22(2):1298–1312.
Banchs, A., De Veciana, G., Sciancalepore, V., and Costa-Perez, X. (2020). Resource allocation for network slicing in mobile networks. IEEE Access, 8:214696–214706.
Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V., and Costa-Pérez, X. (2020). A machine learning approach to 5g infrastructure market optimization. IEEE Transactions on Mobile Computing, 19(3):498–512.
Choi, J., Krishnan, S., and Park, J. (2024). Latency-optimal resource allocation for uav-aided leo communication. IEEE Transactions on Vehicular Technology, 73(8):12096–12108.
del Prever, P. B., D’Oro, S., Bonati, L., Polese, M., Tsampazi, M., Lehmann, H., and Melodia, T. (2025). Pacifista: Conflict evaluation and management in open ran. IEEE Transactions on Mobile Computing.
ETSI / 3GPP (2022). Etsi ts 129 536 v17.2.0 (2022-10): 5g; 5g system; network slice admission control services; stage 3 (3gpp ts 29.536 version 17.2.0 release 17). Technical Specification TS 129 536 V17.2.0, European Telecommunications Standards Institute. Accessed: 2025-01-14.
Fidler, M. and Rizk, A. (2015). A guide to the stochastic network calculus. IEEE Communications Surveys & Tutorials, 17(1):92–105.
García-Morales, J., Lucas-Estañ, M. C., and Gozalvez, J. (2019). Latency-sensitive 5g ran slicing for industry 4.0. IEEE Access, 7:143139–143159.
Guo, C., Liang, L., and Li, G. Y. (2019). Resource allocation for high-reliability low-latency vehicular communications with packet retransmission. IEEE Transactions on Vehicular Technology, 68(7):6219–6230.
Guo, T. and Suárez, A. (2019). Enabling 5g ran slicing with edf slice scheduling. IEEE Transactions on Vehicular Technology, 68(3):2865–2877.
Le Boudec, J.-Y. and Thiran, P. (2001). Network Calculus: A Theory of Deterministic Queuing Systems for the Internet. Springer.
Patra, M., Thakur, R., and Murthy, C. S. R. (2017). Improving delay and energy efficiency of vehicular networks using mobile femto access points. IEEE Transactions on Vehicular Technology, 66(2):1496–1505.
Rocha, F. G., Almeida, G. M., Cardoso, K. V., Both, C. B., and De Rezende, J. F. (2026). Optimal Resource Allocation with Delay Guarantees for Network Slicing in Disaggregated RAN. IEEE/ACM Transactions on Networking, 34:4249–4268.
Rocha, F. G. C. and Vieira, F. H. T. (2019). A channel and queue-aware scheduling for the lte downlink based on service curve and buffer overflow probability. IEEE Wireless Communications Letters, 8(3):729–732.
Tang, J., Shim, B., and Quek, T. Q. (2019). Service multiplexing and revenue maximization in sliced C-RAN incorporated with URLLC and multicast eMBB. IEEE Journal on Selected Areas in Communications, 37(4):881–895.
Zanzi, L., Sciancalepore, V., Garcia-Saavedra, A., Schotten, H. D., and Costa-Pérez, X. (2021). Laco: A latency-driven network slicing orchestration in beyond-5g networks. IEEE Transactions on Wireless Communications, 20(1):667–682.
Publicado
25/05/2026
Como Citar
SILVA JÚNIOR, Pedro Mendes da; ROCHA, Flávio Geraldo Coelho.
Garantias de Latência sob Incerteza: Planejamento de Fatias URLLC em Redes B5G. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 31. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 99-112.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2026.23735.
