Fair Max Rate: Um Escalonador de Recursos Baseado em Aprendizado por Reforço para Redes 5G
Resumo
Este artigo propõe um escalonador de recursos para redes 5G baseado em aprendizado por reforço, visando maximizar a vazão da rede ao distribuir recursos conforme a eficiência espectral de cada dispositivo de usuário. O escalonador proposto assegura uma vazão mínima para cada dispositivo, equilibrando eficiência e justiça. A avaliação se dá através de simulação com base nas métricas de vazão e Índice de Jain. O desempenho do escalonador proposto é comparado aos algoritmos clássicos Round Robin (RR), Proportional Fair (PF) e Max Rate (MR), e, com ele, se obtém um aumento de até 27,27% na vazão quando comparado ao RR e com melhores índices de justiça quando comparado ao MR.
Palavras-chave:
Escalonador, Inteligência Artificial, Aprendizado por Reforço, Redes Móveis, 5G
Referências
3GPP. (2020a). Physical channels and modulation (3GPP TS 38.211 Version 16.2.0 Release 16). ETSI. Sophia Antipolis, France.
3GPP. (2020b). Physical layer procedures for data (3GPP TS 38.214 Version 16.2.0 Release 16). ETSI. Sophia Antipolis, France.
Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655.
Al-Tam, F., Correia, N., & Rodriguez, J. (2020). Learn to schedule: LEASCH: A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer. IEEE Access, 8, 143063–143076.
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.
Bonati, L., D’Oro, S., Polese, M., Basagni, S., & Melodia, T. (2021). Intelligence and learning in O-RAN for data-driven nextG cellular networks. IEEE Communications Magazine, 59(10), 21–27.
Dahlman, E., Parkvall, S., & Skold, J. (2020). 5G NR: The next generation wireless access technology. Academic Press.
Gu, Z., She, C., Hardjawana, W., Lumb, S., McKechnie, D., Essery, T., & Vucetic, B. (2021). Knowledge-assisted deep reinforcement learning in 5G scheduler design: From theoretical framework to implementation. IEEE Journal on Selected Areas in Communications, 39(7), 2014–2028.
Holma, H., Toskala, A., & Reunanen, J. (2015). LTE small cell optimization: 3GPP evolution to Release 13. Wiley.
Huang, Q., & Kadoch, M. (2020). 5G resource scheduling for low-latency communication: A reinforcement learning approach. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) (pp. 1–5). IEEE.
Jain, R. K., Chiu, D.-M. W., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA, 21, 1.
Patriciello, N., Lagen, S., Bojovic, B., & Giupponi, L. (2019). An E2E simulator for 5G NR networks. Simulation Modelling Practice and Theory, 96, 101933.
Rodrigues, C. F. F., Lovisolo, L., & da Silva Mello, L. (2022). Alocação de recursos da interface aérea 5G a partir de um critério de utilidade. In Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT 2022).
Saraiva, J. V., Jr., I. M. B., Monteiro, V. F., Lima, F. R. M., Maciel, T. F., Jr., W. C. F., & Cavalcanti, F. R. P. (2020). Deep reinforcement learning for QoS-constrained resource allocation in multiservice networks.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Vihriala, J., Zaidi, A. A., Venkatasubramanian, V., He, N., Tiirola, E., Medbo, J., Lahetkangas, E., Werner, K., Pajukoski, K., & Cedergren, A. (2016). Numerology and frame structure for 5G radio access. In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–5).
Yang, L., Jia, J., Lin, H., & Cao, J. (2022). Reliable dynamic service chain scheduling in 5G networks. IEEE Transactions on Mobile Computing, 22(8), 4898–4911.
You, X., Zhang, C., Tan, X., Jin, S., & Wu, H. (2019). AI for 5G: Research directions and paradigms. Science China Information Sciences, 62, 1–13.
Zhang, C., Ueng, Y.-L., Studer, C., & Burg, A. (2020). Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(2), 149–163.
3GPP. (2020b). Physical layer procedures for data (3GPP TS 38.214 Version 16.2.0 Release 16). ETSI. Sophia Antipolis, France.
Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655.
Al-Tam, F., Correia, N., & Rodriguez, J. (2020). Learn to schedule: LEASCH: A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer. IEEE Access, 8, 143063–143076.
Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.
Bonati, L., D’Oro, S., Polese, M., Basagni, S., & Melodia, T. (2021). Intelligence and learning in O-RAN for data-driven nextG cellular networks. IEEE Communications Magazine, 59(10), 21–27.
Dahlman, E., Parkvall, S., & Skold, J. (2020). 5G NR: The next generation wireless access technology. Academic Press.
Gu, Z., She, C., Hardjawana, W., Lumb, S., McKechnie, D., Essery, T., & Vucetic, B. (2021). Knowledge-assisted deep reinforcement learning in 5G scheduler design: From theoretical framework to implementation. IEEE Journal on Selected Areas in Communications, 39(7), 2014–2028.
Holma, H., Toskala, A., & Reunanen, J. (2015). LTE small cell optimization: 3GPP evolution to Release 13. Wiley.
Huang, Q., & Kadoch, M. (2020). 5G resource scheduling for low-latency communication: A reinforcement learning approach. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) (pp. 1–5). IEEE.
Jain, R. K., Chiu, D.-M. W., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA, 21, 1.
Patriciello, N., Lagen, S., Bojovic, B., & Giupponi, L. (2019). An E2E simulator for 5G NR networks. Simulation Modelling Practice and Theory, 96, 101933.
Rodrigues, C. F. F., Lovisolo, L., & da Silva Mello, L. (2022). Alocação de recursos da interface aérea 5G a partir de um critério de utilidade. In Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT 2022).
Saraiva, J. V., Jr., I. M. B., Monteiro, V. F., Lima, F. R. M., Maciel, T. F., Jr., W. C. F., & Cavalcanti, F. R. P. (2020). Deep reinforcement learning for QoS-constrained resource allocation in multiservice networks.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Vihriala, J., Zaidi, A. A., Venkatasubramanian, V., He, N., Tiirola, E., Medbo, J., Lahetkangas, E., Werner, K., Pajukoski, K., & Cedergren, A. (2016). Numerology and frame structure for 5G radio access. In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–5).
Yang, L., Jia, J., Lin, H., & Cao, J. (2022). Reliable dynamic service chain scheduling in 5G networks. IEEE Transactions on Mobile Computing, 22(8), 4898–4911.
You, X., Zhang, C., Tan, X., Jin, S., & Wu, H. (2019). AI for 5G: Research directions and paradigms. Science China Information Sciences, 62, 1–13.
Zhang, C., Ueng, Y.-L., Studer, C., & Burg, A. (2020). Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(2), 149–163.
Publicado
19/05/2025
Como Citar
LOPES, Diego Canizio; NASSERALA, André; BASTOS, Ian Vilar; MORAES, Igor M..
Fair Max Rate: Um Escalonador de Recursos Baseado em Aprendizado por Reforço para Redes 5G. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 434-447.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.6223.
