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ORANUS: Latency-tailored Orchestration via Stochastic Network Calculus in 6G O-RAN

Published 8 Jan 2024 in cs.NI | (2401.03812v1)

Abstract: The Open Radio Access Network (O-RAN)-compliant solutions lack crucial details to perform effective control loops at multiple time scales. In this vein, we propose ORANUS, an O-RAN-compliant mathematical framework to allocate radio resources to multiple ultra Reliable Low Latency Communication (uRLLC) services. In the near-RT control loop, ORANUS relies on a novel Stochastic Network Calculus (SNC)-based model to compute the amount of guaranteed radio resources for each uRLLC service. Unlike traditional approaches as queueing theory, the SNC-based model allows ORANUS to ensure the probability the packet transmission delay exceeds a budget, i.e., the violation probability, is below a target tolerance. ORANUS also utilizes a RT control loop to monitor service transmission queues, dynamically adjusting the guaranteed radio resources based on detected traffic anomalies. To the best of our knowledge, ORANUS is the first O-RAN-compliant solution which benefits from SNC to carry out near-RT and RT control loops. Simulation results show that ORANUS significantly improves over reference solutions, with an average violation probability 10x lower.

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References (38)
  1. P. Popovski et al., “Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks,” IEEE Netw., vol. 32, no. 2, pp. 16–23, 2018.
  2. B. Tang, V. K. Shah, V. Marojevic, and J. H. Reed, “AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities,” IEEE Wirel. Commun., vol. 30, no. 1, pp. 70–77, 2023.
  3. A. S. Abdalla, P. S. Upadhyaya, V. K. Shah, and V. Marojevic, “Toward Next Generation Open Radio Access Networks: What O-RAN Can and Cannot Do!,” IEEE Netw., vol. 36, no. 6, pp. 206–213, 2022.
  4. M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges,” IEEE Commun. Surv. Tutor., pp. 1–1, 2023.
  5. S. D’Oro, M. Polese, L. Bonati, H. Cheng, and T. Melodia, “dApps: Distributed Applications for Real-Time Inference and Control in O-RAN,” IEEE Commun. Mag., vol. 60, no. 11, pp. 52–58, 2022.
  6. L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, H. D. Schotten, and X. Costa-Pérez, “LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks,” IEEE Trans. Wirel. Commun., vol. 20, no. 1, pp. 667–682, 2021.
  7. C. Xiao et al., “Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications,” IEEE J. Sel. Areas Commun., vol. 37, no. 4, pp. 780–794, 2019.
  8. S. Schiessl, M. Skoglund, and J. Gross, “NOMA in the Uplink: Delay Analysis With Imperfect CSI and Finite-Length Coding,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 3879–3893, 2020.
  9. Á. A. Cardoso, M. V. G. Ferreira, and F. H. T. Vieira, “Delay bound estimation for multicarrier 5G systems considering lognormal beta traffic envelope and stochastic service curve,” Trans. Emerg. Telecommun. Technol., p. e4281, 2021.
  10. O. Adamuz-Hinojosa, V. Sciancalepore, P. Ameigeiras, J. M. Lopez-Soler, and X. Costa-Pérez, “A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices,” IEEE Trans. Wirel. Commun., vol. 22, no. 2, pp. 1250–1265, 2023.
  11. O-RAN Working Group 3, “Near-Real-time RAN Intelligent Controller, Use Cases and Requirements 3.0,” Mar. 2023.
  12. O-RAN Working Group 1, “Use Cases Analysis Report 10.0,” Mar. 2023.
  13. M. Fidler and A. Rizk, “A Guide to the Stochastic Network Calculus,” IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 92–105, 2015.
  14. S. Ross, A First Course in Probability. Pearson, 2014.
  15. C. M. Bishop, “Mixture Density Networks,” 1994.
  16. B. Selim, O. Alhussein, S. Muhaidat, G. K. Karagiannidis, and J. Liang, “Modeling and Analysis of Wireless Channels via the Mixture of Gaussian Distribution,” IEEE Trans. Veh. Technol., vol. 65, no. 10, pp. 8309–8321, 2016.
  17. K. Plataniotis and D. Hatzinakos, “Gaussian Mixtures and Their Applications to Signal Processing,” Advanced Signal Processing Handbook, CRC Press, pp. 89–124, 2017.
  18. G. J. McLachlan, S. X. Lee, and S. I. Rathnayake, “Finite Mixture Models,” Annual Review of Statistics and Its Application, vol. 6, no. 1, pp. 355–378, 2019.
  19. T. Guo and A. Suárez, “Enabling 5G RAN Slicing With EDF Slice Scheduling,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 2865–2877, 2019.
  20. A. Elgabli, H. Khan, M. Krouka, and M. Bennis, “Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks,” in 2019 IEEE ISCC, pp. 1–6, 2019.
  21. R. Falkenberg and C. Wietfeld, “FALCON: An Accurate Real-Time Monitor for Client-Based Mobile Network Data Analytics,” in 2019 IEEE GLOBECOM, pp. 1–7, 2019.
  22. M. E. Haque, F. Tariq, M. R. A. Khandaker, K.-K. Wong, and Y. Zhang, “A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems,” IEEE Access, vol. 11, pp. 34372–34396, 2023.
  23. M. U. A. Siddiqui, H. Abumarshoud, L. Bariah, S. Muhaidat, M. A. Imran, and L. Mohjazi, “URLLC in Beyond 5G and 6G Networks: An Interference Management Perspective,” IEEE Access, vol. 11, pp. 54639–54663, 2023.
  24. B. S. Khan, S. Jangsher, A. Ahmed, and A. Al-Dweik, “URLLC and eMBB in 5G Industrial IoT: A Survey,” IEEE Open J. Commun. Soc., vol. 3, pp. 1134–1163, 2022.
  25. S. F. Abedin, A. Mahmood, N. H. Tran, Z. Han, and M. Gidlund, “Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach,” IEEE Trans. Veh. Technol., vol. 71, no. 10, pp. 10808–10822, 2022.
  26. M. Karbalaee Motalleb, V. Shah-Mansouri, S. Parsaeefard, and O. L. Alcaraz López, “Resource Allocation in an Open RAN System Using Network Slicing,” IEEE Trans. Netw. Service Manag., vol. 20, no. 1, pp. 471–485, 2023.
  27. F. Rezazadeh, L. Zanzi, F. Devoti, H. Chergui, X. Costa-Pérez, and C. Verikoukis, “On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration,” IEEE Trans. Veh. Technol., vol. 72, no. 3, pp. 3473–3487, 2023.
  28. M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms,” IEEE Trans. Mob. Comput., pp. 1–14, 2022.
  29. W. Zhang, M. Derakhshani, G. Zheng, C. S. Chen, and S. Lambotharan, “Bayesian Optimization of Queuing-Based Multichannel URLLC Scheduling,” IEEE Trans. Wirel. Commun., vol. 22, no. 3, pp. 1763–1778, 2023.
  30. B. Shi, F.-C. Zheng, C. She, J. Luo, and A. G. Burr, “Risk-Resistant Resource Allocation for eMBB and URLLC Coexistence Under M/G/1 Queueing Model,” IEEE Trans. Veh. Technol., vol. 71, no. 6, pp. 6279–6290, 2022.
  31. P. Yang, X. Xi, T. Q. S. Quek, J. Chen, X. Cao, and D. Wu, “RAN Slicing for Massive IoT and Bursty URLLC Service Multiplexing: Analysis and Optimization,” IEEE Internet Things J., vol. 8, no. 18, pp. 14258–14275, 2021.
  32. I. Hadar, L.-O. Raviv, and A. Leshem, “Scheduling For 5G Cellular Networks With Priority And Deadline Constraints,” in IEEE ICSEE, pp. 1–5, 2018.
  33. L.-O. Raviv and A. Leshem, “Joint Scheduling and Resource Allocation for Packets With Deadlines and Priorities,” IEEE Commun. Lett., vol. 27, no. 1, pp. 248–252, 2023.
  34. F. Capozzi, G. Piro, L. Grieco, G. Boggia, and P. Camarda, “Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey,” IEEE Commun. Surv. Tutor., vol. 15, no. 2, pp. 678–700, 2013.
  35. J. Zhang, X. Xu, K. Zhang, B. Zhang, X. Tao, and P. Zhang, “Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario,” IEEE Access, vol. 7, pp. 65811–65820, 2019.
  36. A. A. Esswie, K. I. Pedersen, and P. E. Mogensen, “Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks,” IEEE Access, vol. 8, pp. 132922–132936, 2020.
  37. M. Alsenwi, N. H. Tran, M. Bennis, S. R. Pandey, A. K. Bairagi, and C. S. Hong, “Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach,” IEEE Trans. Wirel. Commun., vol. 20, no. 7, pp. 4585–4600, 2021.
  38. O-RAN Working Group 2, “O-RAN AI/ML workflow description and requirements 1.03,” July 2021.
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