Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach
Open accessAbstract
The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.
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The Shapley additive explanations (SHAP) method is particularly effective for feature selection, as it assigns importance values to each feature based on their contribution to the model's predictions, allowing for the exclusion of less significant features without sacrificing performance.
“The Shapley additive explanations (SHAP) method is particularly effective for feature selection, as it assigns importance values to each feature based on their contribution to the model's predictions, allowing for the exclusion of less significant features without sacrificing performance [20].”
From Explainable AI-Aided Feature Selection and Model Reduction for DRL-Based V2X Resource Allocation (2025) · cites this paper as [20]
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Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Consistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · see citing works
Network Slicing in 5G: Survey and Challenges
Foukas, X., Patounas, G., Elmokashfi, A. M., & Marina, M. K. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600951 · Cited by 1,173 · see citing works
AI-Assisted Network-Slicing Based Next-Generation Wireless Networks
Shen, X., Gao, J., Wu, W. R., Lyu, K., Li, M., Zhuang, W., Li, X., & Rao, J. B. (2020) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2020.2965100 · Cited by 343 · see citing works
DeepCog: Optimizing Resource Provisioning in Network Slicing With AI-Based Capacity Forecasting
Bega, D., Gramaglia, M., Fiore, M., Banchs, A., & Costa‐Pérez, X. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2959245 · Cited by 138 · see citing works
Sun, G., Gebrekidan, T. Z., Boateng, G. O., Ayepah-Mensah, D., & Jiang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2909670 · Cited by 127 · see citing works
Debugging Tests for Model Explanations
Adebayo, J., Muelly, M., Liccardi, I., & Kim, B. (2020) · arXiv · doi:10.48550/arxiv.2011.05429 · Cited by 88 · see citing works
Li, C., Guo, W., Sun, S. C., Al–Rubaye, S., & Tsourdos, A. (2020) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2020.3017181 · Cited by 76 · see citing works
Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network
Terra, A., Inam, R., Baskaran, S., Batista, P., Burdick, I., & Fersman, E. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322496 · Cited by 50 · see citing works
Guo, Q., Gu, R., Wang, Z., Zhao, T., Ji, Y., Kong, J., Gour, R., & Jue, J. P. (2019) · Optical Fiber Communication Conference · doi:10.1364/ofc.2019.w3j.3 · Cited by 25 · see citing works
Discovering Mobile Application Usage Patterns from a Large-Scale Dataset
Silva, F. A., Domingues, A. C. S. A., & Silva, T. R. M. B. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3209669 · Cited by 24 · see citing works
Resource Reservation within Sliced 5G Networks: A Cost-Reduction Strategy for Service Providers
Monteil, J.-B., Hribar, J., Barnard, P., Li, Y., & DaSilva, L. A. (2020) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops49005.2020.9145374 · Cited by 14 · see citing works
Self-citation5G Traffic Forecasting: If Verticals and Mobile Operators Cooperate
Malandrino, F., & Chiasserini, C. F. (2019) · Annual Conference on Wireless On-demand Network Systems and Services (WONS) · doi:10.23919/wons.2019.8795501 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [17]17. A. Adams and P. Vamplew, "Encoding and decoding cyclic data," The South Pacific Journal of Natural Science, vol. 16, 1998.
Cited by
Sorted by citations
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170 · see citing works
Lee, J., Solat, F., Kim, T. Y., & Poor, H. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3352910 · Cited by 111 · see citing works
Advancing 6G: Survey for Explainable AI on Communications and Network Slicing
Sun, H., Liu, Y., Al-Tahmeesschi, A., Nag, A., Soleimanpour-Moghadam, M., Canberk, B., Arslan, H., & Ahmadi, H. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3534626 · Cited by 111 · see citing works
Brik, B., Chergui, H., Zanzi, L., Devoti, F., Ksentini, A., Siddiqui, M. S., Costa‐Pérez, X., & Verikoukis, C. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3510543 · Cited by 84 · see citing works
A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?
Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 32 · see citing works
Ayoub, O., Cicco, N. D., Ezzeddine, F., Bruschetta, F., Rubino, R., Nardecchia, M., Milano, M., Musumeci, F., Passera, C., & Tornatore, M. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109466 · Cited by 28 · see citing works
Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing
Rezazadeh, F., Chergui, H., & Mangues‐Bafalluy, J. (2023) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops57953.2023.10283684 · Cited by 28 · see citing works
Machine Learning With Computer Networks: Techniques, Datasets, and Models
Afifi, H., Pochaba, S., Boltres, A., Laniewski, D., Haberer, J., Paeleke, L., Poorzare, R., Stolpmann, D., Wehner, N., Redder, A., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3384460 · Cited by 32 · see citing works
Toward Bridging the FL Performance-Explainability Tradeoff: A Trustworthy 6G RAN Slicing Use-Case
Roy, S., Chergui, H., & Verikoukis, C. V. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3364363 · Cited by 25 · see citing works
Explainable AI-Aided Feature Selection and Model Reduction for DRL-Based V2X Resource Allocation
Khan, N., Abdallah, A., Celik, A., Eltawil, A. M., & Coleri, S. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3554655 · Cited by 20 · see citing works
Explainable Artificial Intelligence for Energy-Efficient Radio Resource Management
Marcu, A.-D., Peesapati, S. K. G., Cortes, J. M., Imtiaz, S., & Gross, J. (2023) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc55385.2023.10119130 · Cited by 15 · see citing works
Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation
Gizzini, A. K., Medjahdi, Y., Ghandour, A. J., & Clavier, L. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3596548 · Cited by 9 · see citing works
Goścień, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125541 · Cited by 7 · see citing works
Leveraging Explainable AI for Reducing Queries of Performance Indicators in Open RAN
Tassie, C., Kim, B., Groen, J., Belgiovine, M., & Chowdhury, K. R. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622827 · Cited by 7 · see citing works
XAI-driven Model Design for Resource Utilization Forecasting in Cloud-native 6G Networks
Liatsas, L., Kibalya, G., & Antonopoulos, A. (2024) · IEEE International Mediterranean Conference on Communications and Networking (MeditCom) · doi:10.1109/meditcom61057.2024.10621360 · Cited by 5 · see citing works
Allias, N., Kadir, D. A., Abdullahi, A. M., & Ismail, S. (2024) · International Conference on Digital Transformation and Applications (ICDXA) · doi:10.1109/icdxa61007.2024.10470665 · Cited by 4 · see citing works
5G Network Slicing Prediction Using Explainable Artificial Intelligence(X-AI)
Maduranga, M., Lakmal, I., Oruthota, U. S., Tilwari, V., Sharma, D., & Gayan, S. (2025) · doi:10.1109/icsc64553.2025.10968200 · Cited by 3 · see citing works
From Black Box to Transparency: Consistency and Cost within XAI
Corona, J., Rodrigues, P., Almeida, L., Teixeira, R., Antunes, M., & Aguiar, R. L. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100910 · Cited by 3 · see citing works
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization
Meer, I. A., Hörmann, B., Özger, M., Geyer, F., Viseras, A., Schupke, D., & Çavdar, Ç. (2025) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc62392.2025.11274880 · Cited by 3 · see citing works
Prioritized Value-Decomposition Network for Explainable AI-Enabled Network Slicing
Salehi, S., Iturria-Rivera, P. E., Elsayed, M., Bavand, M., Gaigalas, R., Ozcan, Y., & Erol‐Kantarci, M. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161775 · Cited by 2 · see citing works
Zilli, C., Sacco, A., Esposito, F., & Marchetto, G. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3567654 · Cited by 1 · see citing works
Explainable AI for Resource-Efficient Microservice Management in Cloud-Native Networks
Liatsas, L., Kibalya, G., & Antonopoulos, A. (2025) · IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) · doi:10.1109/icmlcn64995.2025.11140124 · Cited by 1 · see citing works
Explainable Artificial Intelligence for Computation Offloading Optimization
Amarasooriya, R., Gregory, M. A., & Li, S. (2024) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac62915.2024.10815558 · Cited by 1 · see citing works
XAI for wireless communications
Gizzini, A. K., Medjahdi, Y., Ghandour, A. J., Clavier, L., & Mabrouk, M. B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-329135-7.00014-x · Cited by 1 · see citing works
From Black Box to Transparency: the hidden costs of XAI in NGNPreprint
Corona, J., Rodrigues, P., Almeida, L., Teixeira, R., Antunes, M., & Aguiar, R. L. (2023) · Research Square · doi:10.21203/rs.3.rs-3714524/v1
Applications of Hybrid and Explainable AI Models for Public Employment ServicesPreprint
Kooistra, J., Machado, M., Misheva, B. H., & Moreira, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6115035
Moudgil, A., Rani, S., & Khan, F. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354977
Interpretable Scheduling in Radio Access Networks via Automata Learning
Sonee, A., Fatehi, K., Yadav, P., Călinescu, R., Ahmadi, H., & Russo, A. M. (2026) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc69400.2026.11711952
Research on Enterprise Communication Network Resource Leasing and Operation and Maintenance System
Hua, X., Shi, Y., & Fei, H.-M. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-34048-1_8
- Authors
- Barnard, P., Macaluso, I., Marchetti, N., & DaSilva, L. A.
- Affiliations
- Trinity College Dublin (IE)
- Trinity College,Connect Research Centre,Dublin,Ireland
- Virginia Tech (US)
- Venue
- ICC 2022 - IEEE International Conference on Communications
- Publisher
- IEEE
- Published
- 2022-05-16
- Pages
- 1530-1535
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W4290996367
- Cited by
- 29
- References
- 16
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 12 · 2023: 3 · 2022: 1
- FWCI
- 3.46
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- International Conference on Communications (publishedVersion)
- VTechWorks (Virginia Tech) (submittedVersion, public-domain)
- Funders
- Science Foundation Ireland
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Keywords
- network slicing, resource reservation, explainable AI, 5G, wireless resource management, model interpretability, explanation fidelity, human-in-the-loop decision making
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