LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks
<p dir="ltr">Managing the growing complexity of Self-Organizing Networks (SONs) in next-generation communication systems requires agile, real-time strategies that can adapt to multidimensional and highly dynamic conditions. Traditional SON management rooted in centralized, rule-based...
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2025
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| _version_ | 1864513524549550080 |
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| author | Adnan Qayyum (16875936) |
| author2 | Abdullatif Albaseer (16904607) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) Mohamed Abdallah (3073191) |
| author2_role | author author author author |
| author_facet | Adnan Qayyum (16875936) Abdullatif Albaseer (16904607) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) Mohamed Abdallah (3073191) |
| author_role | author |
| dc.creator.none.fl_str_mv | Adnan Qayyum (16875936) Abdullatif Albaseer (16904607) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) Mohamed Abdallah (3073191) |
| dc.date.none.fl_str_mv | 2025-09-18T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/mnet.2025.3605319 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/LLM-Driven_Multi-Agent_Architectures_for_Intelligent_Self-Organizing_Networks/31056577 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Complexity theory Collaboration Cognition Security Real-time systems Decision making Quality of service Optimization Adaptation models 6G mobile communication |
| dc.title.none.fl_str_mv | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Managing the growing complexity of Self-Organizing Networks (SONs) in next-generation communication systems requires agile, real-time strategies that can adapt to multidimensional and highly dynamic conditions. Traditional SON management rooted in centralized, rule-based, and static models, struggles to meet these evolving requirements. Recent advances in multi-agent systems (MAS) and Large Language Models (LLMs) enable the design of intelligent and context-aware frameworks that span multiple operational layers. In this paper, we introduce LaMA-SON, an LLM-driven MAS for intelligent SON management. Specifically, LaMA-SON incorporates specialized agents to handle three critical operational functions: traffic management, quality of service (QoS) optimization, and security threat detection. We perform a proof-of-concept evaluation using a real-world network traffic classification dataset, where traffic, security, and QoS optimization agents make decisions based on role-specific features and structured prompts. Our experimental results demonstrate that LaMA-SON achieves higher accuracy and recall while preserving balanced precision-recall trade-offs and outperforms standalone LLM baselines, highlighting the benefits of multi-agent collaboration. Finally, we highlight various open research challenges that require further investigation to fully realize the potential of LLM-based MAS frameworks in network operations management.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Network<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/mnet.2025.3605319" target="_blank">https://dx.doi.org/10.1109/mnet.2025.3605319</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ce088047b8ff98420e39a75b61d54a1e |
| identifier_str_mv | 10.1109/mnet.2025.3605319 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31056577 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing NetworksAdnan Qayyum (16875936)Abdullatif Albaseer (16904607)Junaid Qadir (16494902)Ala Al-Fuqaha (4434340)Mohamed Abdallah (3073191)EngineeringCommunications engineeringInformation and computing sciencesArtificial intelligenceMachine learningComplexity theoryCollaborationCognitionSecurityReal-time systemsDecision makingQuality of serviceOptimizationAdaptation models6G mobile communication<p dir="ltr">Managing the growing complexity of Self-Organizing Networks (SONs) in next-generation communication systems requires agile, real-time strategies that can adapt to multidimensional and highly dynamic conditions. Traditional SON management rooted in centralized, rule-based, and static models, struggles to meet these evolving requirements. Recent advances in multi-agent systems (MAS) and Large Language Models (LLMs) enable the design of intelligent and context-aware frameworks that span multiple operational layers. In this paper, we introduce LaMA-SON, an LLM-driven MAS for intelligent SON management. Specifically, LaMA-SON incorporates specialized agents to handle three critical operational functions: traffic management, quality of service (QoS) optimization, and security threat detection. We perform a proof-of-concept evaluation using a real-world network traffic classification dataset, where traffic, security, and QoS optimization agents make decisions based on role-specific features and structured prompts. Our experimental results demonstrate that LaMA-SON achieves higher accuracy and recall while preserving balanced precision-recall trade-offs and outperforms standalone LLM baselines, highlighting the benefits of multi-agent collaboration. Finally, we highlight various open research challenges that require further investigation to fully realize the potential of LLM-based MAS frameworks in network operations management.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Network<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/mnet.2025.3605319" target="_blank">https://dx.doi.org/10.1109/mnet.2025.3605319</a></p>2025-09-18T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/mnet.2025.3605319https://figshare.com/articles/journal_contribution/LLM-Driven_Multi-Agent_Architectures_for_Intelligent_Self-Organizing_Networks/31056577CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/310565772025-09-18T12:00:00Z |
| spellingShingle | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks Adnan Qayyum (16875936) Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Complexity theory Collaboration Cognition Security Real-time systems Decision making Quality of service Optimization Adaptation models 6G mobile communication |
| status_str | publishedVersion |
| title | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| title_full | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| title_fullStr | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| title_full_unstemmed | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| title_short | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| title_sort | LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks |
| topic | Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Complexity theory Collaboration Cognition Security Real-time systems Decision making Quality of service Optimization Adaptation models 6G mobile communication |