SpringerLink
Log in

Use Cases Employing a Machine Learning Network Architecture

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

  • 643 Accesses

Abstract

5G mobile networks will soon be available to handle all types of applications and to provide services to massive numbers of users. In this complex and dynamic network ecosystem, an end-to-end performance analysis and optimisation will be “key” features to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a challenging vision, the MARSAL EU-funded project [1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. In this paper, we present the network architecture of the MARSAL, as well as how the experimentation scenarios are mapped to the considered architecture.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 96.29
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 128.39
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 128.39
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    An SFP is a compact, hot-pluggable network interface module format used for both telecommunication and data communications applications. An SFP interface on networking hardware is a modular slot for a media-specific transceiver, such as for a fiber-optic cable or a copper cable. A GPON (Gigabit Ethernet Passive Optical Network) SFP module transmits and receives signals of different wavelengths between the OLT at the “Central Office” side and the ONT (Optical Network Terminal) at the end-users’ side. GPON SFPs utilize both the upstream data and downstream data by means of Optical Wavelength Division Multiplexing (WDM).

References

  1. MARSAL (“Machine Learning-based Networking and Computing Infrastructure Resource Management of 5G and Beyond Intelligent Networks”) 5G-PPP/H2020 project, Grant Agreement No.101017171. https://www.marsalproject.eu/

  2. Cisco, Cisco Visual Networking Index: Forecast and Trends, 2017–2022 White Paper (2019)

    Google Scholar 

  3. Ericsson Mobility Reports. https://www.ericsson.com/en/mobility-report/reports/november-2019

  4. Chen, S., Liang, Y.-C., Sun, S., et al.: Vision, requirements, and technology trend of 6g: how to tackle the challenges of system coverage, capacity, user data-rate and movement speed. IEEE Wirel. Commun. 27(2), 218–228 (2020)

    Google Scholar 

  5. Chochliouros, I.P., et al.: Energy efficiency concerns and trends in future 5G network infrastructures. Energies (MDPI) 14(17), 5932 (2021). https://doi.org/10.3390/en14175392

  6. Boutaba, R., Salahuddin, M., Limam, N., Ayoubi. S.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 1–99 (2018)

    Google Scholar 

  7. Bhat, J.R., lqahtani, S.A.: 6G ecosystem: current status and future perspective. IEEE Access 9, 43134–43167 (2021)

    Google Scholar 

  8. Nawaz, F., Ibrahim, J., et al.: A review of vision and challenges of 6g technology. Int. J. Adv. Comput. Sci. Appl. 11(2), 1–7 (2020)

    Google Scholar 

  9. David, K., Berndt, H.: 6G vision and requirements: is there any need for beyond 5G? IEEE Veh. Technol. Mag. 13(3), 72–80 (2018)

    Article  Google Scholar 

  10. Shafin, R., Liu, L., et al.: Artificial intelligence-enabled cellular networks: a critical path to beyond-5G and 6G. IEEE Wirel. Commun. 27(2), 212–217 (2020)

    Google Scholar 

  11. Jiang, W., Han, B., Habibi, M.A., et al.: The road towards 6G: a comprehensive survey. IEEE Open J. Commun. Soc. 2, 334–366 (2021)

    Google Scholar 

  12. Open Radio Accees Network Alliance e.V. (O-RAN). https://www.o-ran.org/

  13. Kostopoulos, A., Chochliouros, I.P., et al.: Experimentation scenarios for machine learning-based resource management. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds.) AIAI-2022, AICT, vol. 652, pp. 120–133. Springer, Cham (2022)

    Google Scholar 

  14. The 3rd Generation Partnership Project (3GPP): NG-RAN Architecture. https://www.3gpp.org/news-events/2160-ng_ran_architecture

  15. The 3rd Generation Partnership Project (3GPP): Technical Specification (TS) 38.801 V14.0.0 (2017-03): Study on new radio access technology; Radio access architecture and interfaces (Release 14). 3GPPP (2017)

    Google Scholar 

  16. Saha, R.K., Nanba, S., Nishimura, K., Kim, Y.-B., Yamazaki, K.: RAN architectural evolution framework toward 5G and beyond cellular-an overview. In: Proceedings of the IEEE PIMRC’18, pp. 592–593. IEEE (2018). https://doi.org/10.1109/PIMRC.2018.8580833

  17. Agarwal, V., Sharma, C., Shetty, R., Jangam, A., Asati, R.: A journey towards a converged 5G architecture & beyond. In: Proceedings of the IEEE 5GWF’21, pp. 18–23. IEEE (2010). https://doi.org/10.1109/5GWF52925.2021.00011

  18. Niknam, S., Roy, A., Dhillon, H.S., et al.: Intelligent O-RAN for beyond 5G and 6G wireless networks. In: Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps), pp. 215–220. IEEE (2022). https://doi.org/10.1109/GCWkshps56602.2022.10008676

  19. Demirhan, U., Alkhateeb, A.: Enabling cell-free massive MIMO systems with wireless millimeter wave fronthaul. IEEE Trans. Wirel. Commun. 21(11), 9482–9496 (2022). https://doi.org/10.1109/TWC.2022.3177186

    Article  Google Scholar 

  20. Wang, D., Zhang, C., Du, Y., Zhao, J., et al.: Implementation of a cloud-based cell-free distributed massive MIMO system. IEEE Commun. Mag. 58(8), 61–67 (2020)

    Google Scholar 

  21. Bjornson, E., Sanguinetti, L.: Scalable cell-free massive MIMO systems. IEEE Trans. Commun. 68(7), 4247–4261 (2020)

    Article  Google Scholar 

  22. Blandino, S., et al.: Multi-user hybrid MIMO at 60 GHz using 16-antenna transmitters. IEEE Trans. Circuits Syst. I: Regular Papers 66(2), 848–858 (2019)

    Google Scholar 

  23. Zhang, D., Wang, Y., Li, X., **ang, W.: Hybridly connected structure for hybrid beamforming in mmWave massive MIMO systems. IEEE Trans. Commun. 66(2), 662–674 (2018). https://doi.org/10.1109/TCOMM.2017.2756882

    Article  Google Scholar 

  24. European Telecommunications Standards Institute (ETSI): ETSI GR MEC-017 V1.1.1 (2018-02): Mobile Edge Computing (MEC); Deployment of Mobile Edge Computing in an NFV environment. ETSI (2018)

    Google Scholar 

  25. European Telecommunications Standards Institute: ETSI GS NFV 002 V1.2.1 (2014-12): Network functions virtualisation (NFV); architectural framework. ETSI (2014). https://www.etsi.org/deliver/etsi_gs/NFV/001_099/002/01.02.01_60/gs_nfv002v010201p.pdf

  26. European Telecommunications Standards Institute (ETSI): ETSI GR NFV 001 V1.3.1 (2021–03): Network Functions Virtualisation (NFV); Use Cases. ETSI (2021)

    Google Scholar 

  27. European Telecommunications Standards Institute (ETSI): Multi-access Edge Computing (MEC). https://www.etsi.org/technologies/multi-access-edge-computing

  28. Chataut, R., Akl, R.: Massive MIMO systems for 5G and beyond networks - overview, recent trends, challenges, and future research direction. Sensors (MDPI) 20(10), 2753 (2020). https://doi.org/10.3390/s20102753

    Article  Google Scholar 

  29. Zhang, P., Willems, F.M.J.: On the downlink capacity of cell-free massive MIMO with constrained fronthaul capacity. Entropy (MDPI) 22(4), 418 (2020)

    Article  MathSciNet  Google Scholar 

  30. Interdonato, G., et al.: Ubiquitous cell-free massive MIMO communications. EURASIP J. Wirel. Commun. Netw. 1, 1–13 (2019)

    Google Scholar 

  31. Elhoushy, S., Ibrahim, M., Hamouda, W.: cell-free massive MIMO: a survey. IEEE Commun. Surv. Tutor. 24(1), 492–523 (2022)

    Article  Google Scholar 

  32. Ngo, H.Q., Ashikhmin, A., et al.: Cell-free massive MIMO versus small cells. IEEE Trans. Wirel. Commun. 16(3), 1834–1850 (2017)

    Google Scholar 

  33. Ngo, H.Q., Ashikhmin, A., et al.: Cell-free massive MIMO: uniformly great service for everyone. In: Proceedings of the IEEE SPAWC’15, pp. 201–205. IEEE (2015)

    Google Scholar 

  34. Liu, W., Han, S., Yang, C.: Energy efficiency comparison of massive MIMO and small cell network. In: Proceedings of the IEEE GlobalSIP’14, pp. 617–621. IEEE (2014)

    Google Scholar 

  35. Mowla, M.M., Ahmad, I., et al.: A green communication model for 5G systems. IEEE Trans. Green Commun. Network. 1(3), 264–280 (2017)

    Google Scholar 

  36. Hamza, A.S., Khalifa, S.S., Hamza, H.S., Elsayed, K.: A survey on inter-cell interference coordination techniques in OFDMA-based cellular networks. IEEE Commun. Surv. Tutor. 15(4), 1642–1670 (2013)

    Article  Google Scholar 

  37. Femenias, G., Riera-Palou, F.: Cell-free millimeter-wave massive MIMO systems with limited fronthaul capacity. IEEE Access 7, 44596–44612 (2019)

    Google Scholar 

  38. Kassam, J., Castanheira, D., et al.: A review of cell-free massive MIMO systems. Electronics (MDPI) 12(4), 1001 (2023). https://doi.org/10.3390/electronics12041001

  39. Zhang, J., et al.: Spectral and energy efficiency of cell-free massive MIMO systems with hardware impairments. In: Proceedings of the IEEE WCSP’17, pp. 1–6. IEEE (2017)

    Google Scholar 

  40. Open Radio Access Network Alliance e.V. (O-RAN): O-RAN Alliance Specifications. https://www.o-ran.org/specifications

  41. Vardakas, J.S., Ramantas, K., Vinogradov, E., et al.: Machine learning-based cell-free support in the O-RAN architecture: an innovative converged optical-wireless solution toward 6G networks. IEEE Wirel. Commun. 29(5), 20–26 (2022)

    Google Scholar 

  42. Rommel, S., Thiago Raddo, R., Monroy, I.T.: The fronthaul infrastructure of 5G mobile networks. In: Proceedings of the IEEE CAMAD’18, pp. 1–6. IEEE (2018)

    Google Scholar 

  43. The 3rd Generation Partnership Project (3GPP). https://www.3gpp.org/

  44. Small Cell Forum (SCF). https://www.smallcellforum.org/

  45. Open Radio Access Network Alliance e.V. (O-RAN): O-RAN Architecture Description: Technical Specification (TS) O-RAN.WG1.OAD-R003scription-v08.00 (2023). https://orandownloadsweb.azurewebsites.net/specifications

  46. MARSAL Project: Deliverable 2.1: Description and definition of targeted PoCS (2021). https://www.marsalproject.eu/wp-content/uploads/2021/09/D2.1-Marsal-final.pdf

  47. Pfeiffer, T.: Next generation mobile fronthaul and midhaul architectures. J. Opt. Commun. Network. 7(11), B38–B45 (2015)

    Article  Google Scholar 

  48. Furtado, L., Fernandes, A., Ohashi, A., Farias, F., Cavalcante, A., Costa, J.: Cell-free massive MIMO deployments: fronthaul topology options and techno-economic aspects. In: Proceedings of the EuCAP’22, pp. 1–5. IEEE (2022). https://doi.org/10.23919/EuCAP53622.2022.9768969

  49. Farias, F., et al.: Cost- and energy-efficient backhaul options for heterogeneous mobile network deployments. Photon Netw. Commun. 32(3), 422–437 (2016). https://doi.org/10.1007/s11107-016-0676-6

  50. Song, D., Zhang, J., **ao, Y., Wang, X., Ji, Y.: Energy optimization with passive WDM based fronthaul in heterogeneous cellular networking. In: Proceedings of the IEEE ACP’18, pp. 1–3, IEEE (2018)

    Google Scholar 

  51. Levi, D.: The Case for Using PON for 5G Fronthaul. Lightwave (2020). https://www.lightwaveonline.com/5g-mobile/article/14188094/the-case-for-using-pon-for-5g-fronthaul

  52. Hexa-X Project: Deliverable 1.2: Expanded 6G vision, use cases and societal values – including aspects of sustainability, security and spectrum (2021). https://hexa-x.eu/wp-content/uploads/2021/05/Hexa-X_D1.2.pdf

  53. The 3rd Generation Partnership Project (3GPP): Technical Specification (TS) 23.501 V18.0.0 (2022–12): System architecture for the 5G System (5GS); Stage 2 (Release 18). 3GPP (2022)

    Google Scholar 

  54. European Union: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation - GDPR). Off. J. L119, 1–88 (2016)

    Google Scholar 

  55. Letaief, K.B., Chen, W., Shi, Y., Zhang, J., Zhang, Y.-J.A.: The roadmap to 6G: AI empowered wireless networks. IEEE Commun. Mag. 57(8), 84–90 (2019)

    Article  Google Scholar 

  56. Zong, B., et al.: 6G technologies: key drivers, core requirements, system architectures, and enabling technologies. IEEE Veh. Technol. Mag. 14(3), 18–27 (2019)

    Google Scholar 

  57. Zhang, Z., **ao, Y., et al.: 6G wireless networks: vision, requirements, architecture, and key technologies. IEEE Veh. Technol. Mag. 14(3), 28–41 (2019)

    Google Scholar 

  58. Sekaran, R., Patan, R., et al.: Survival study on blockchain based 6G-enabled mobile edge computation for IoT automation. IEEE Access 8, 143453–143463 (2020)

    Google Scholar 

  59. Dai, Y., Xu, D., Maharjan, S., Chen, Z., He, Q., et al.: Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE Netw. 33(3), 10–17 (2019)

    Google Scholar 

  60. European Commission: The Next Generation Internet initiative. https://digital-strategy.ec.europa.eu/en/policies/next-generation-internet-initiative

  61. Engelbart, D.C.: Augmenting Human Intellect: A Conceptual Framework. Menlo Park, CA (1962)

    Google Scholar 

  62. National Research Council: Complex Operational Decision Making in Networked Systems of Humans and Machines: A Multidisciplinary Approach. National Academies Press (2014)

    Google Scholar 

  63. National Aeronautics and Space Administration (NASA). https://www.nasa.gov/centers/ames/research/technology-onepagers/hc-computing.html

  64. Sebe, N.: Human-centered Computing. In: Nakashima, H., et al. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 349–370. Springer, Boston (2010)

    Google Scholar 

  65. Ayala-Romero, J.A., Garcia-Saavedra, A., Gramaglia, M., Costa-Perez, X., Banchs, A., Alcaraz, J.J.: vrAIn: A deep learning approach tailoring computing and radio resources in virtualized RANs. In: Proceedings of MobiCom’19, pp. 1–16. ACM (2019)

    Google Scholar 

  66. García-Durán, A., Niepert, M.: Learning graph representations with embedding propagation. In: Advances in Neural Information Processing Systems, vol. 30, pp. 5119–5130 (2017)

    Google Scholar 

  67. Liu, J., Liu, Z.: A survey on security verification of blockchain smart contracts. IEEE Access 7, 77894–77904 (2019)

    Article  Google Scholar 

Download references

Acknowledgments

The paper has been based on the context of the “MARSAL” (“Machine Learning-Based, Networking and Computing Infrastructure Resource Management of 5G and Beyond Intelligent Networks”) Project, funded by the EC under the Grant Agreement (GA) No.101017171.

Author information

Authors and Affiliations

  1. Hellenic Telecommunications Organization (OTE) S.A., 99 Kifissias Avenue, 15124, Maroussi, Athens, Greece

    Ioannis P. Chochliouros & Christina Lessi

  2. Iquadrat Informatica SL, Barcelona, Spain

    John Vardakas & Christos Verikoukis

  3. IS-Wireless, Piaseczno, Poland

    Md Arifur Rahman

  4. Katholieke Universiteit Leuven, Leuven, Belgium

    Andrea P. Guevara & Robbert Beerten

  5. Orange S.A., Paris, France

    Philippe Chanclou

  6. NEC Laboratories Europe GmbH, Heidelberg, Germany

    Roberto Gonzalez

  7. eBOS Technologies Limited, Nicosia, Cyprus

    Charalambos Klitis

  8. Universita Degli Studi Di Milano, Milan, Italy

    Pierangela Samarati

  9. Institute of Communications and Computer Systems (ICCS), Athens, Greece

    Polyzois Soumplis & Emmanuel Varvarigos

  10. Intracom S.A. Telecom Solutions, Peania, Greece

    Dimitrios Kritharidis & Kostas Chartsias

Authors
  1. Ioannis P. Chochliouros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Vardakas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christos Verikoukis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Md Arifur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrea P. Guevara
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robbert Beerten
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Philippe Chanclou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Roberto Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charalambos Klitis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pierangela Samarati
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Polyzois Soumplis
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Emmanuel Varvarigos
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dimitrios Kritharidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kostas Chartsias
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Christina Lessi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ioannis P. Chochliouros .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

Rights and permissions

Reprints and permissions

Copyright information

© 2023 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chochliouros, I.P. et al. (2023). Use Cases Employing a Machine Learning Network Architecture. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34171-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34170-0

  • Online ISBN: 978-3-031-34171-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation