A two-part tutorial-and-survey is presented that formalises the control, management, and AI-native planes of 5G and 6G, and maps agentic capabilities onto 5G/6G control surfaces, standardization, and major 6G initiatives.
Abstract
Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs). Existing surveys treat the two domains in isolation, leaving protocol integration, evaluation, and standardization alignment underexplored. To address this gap, a two-part tutorial-and-survey is presented. Part I formalises the control, management, and AI-native planes of 5G and 6G. It then covers the foundations of agentic systems: reasoning, planning, tool use, multi-agent coordination, and evaluation. Part II maps agentic capabilities onto 5G/6G control surfaces, standardization, and major 6G initiatives. Finally, it identifies open challenges shaping autonomous telecommunications.
This work introduces Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self reflection, self-orienting, and self-architecting capabilities, and concludes with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.
P. Djukic, Sudipta Acharya, Takai-Eddine Kennouche et al.· IEEE Network· 0 citations
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations
Large Language Models (LLMs) are proposed as control interfaces for next-generation networks, but their latency, hallucinations, and lack of control guarantees make them unsuitable for near-real-time packet schedulers, especially in dynamic V2X environments. This paper introduces Agentic-V2X, an architecture where a small, locally deployed language model acts as a periodic non-real-time rApp-inspired policy creator, while a lightweight xApp-like controller executes validated policies at intervals suitable for scheduling. The framework targets deadline-aware 5G NR V2X scheduling with heterogeneous services (teleoperated driving, cooperative awareness, HD map sharing, and sensor sharing). Given a scenario summary, service objective, and telemetry, the LLM generates a structured policy containing service priorities, weight bounds, and safety constraints. A validator checks and repairs the policy before the controller enforces it via scheduler-weight adaptation in ns-3/ns3-ai. The evaluation compares proportional fair scheduling, static expert policies, a heuristic xApp, static LLM policies, and adaptive LLM-rApp policies over 126 completed runs. Metrics include deadline-constrained packet reception ratio, tail latency, deadline violations, throughput, fairness, policy validity, and safety interventions. Results show that the adaptive LLM-rApp/xApp design generates valid and executable policies and remains competitive at several operating points, including improved mean critical reliability over PF at the highest density. However, paired statistical analysis shows that the adaptive method is not the best aggregate method and remains below the strongest static policies overall. These results support Agentic-V2X as a safe, executable small-LLM policy-generation architecture rather than a universally dominant scheduler.
Gerasimos Papanikolaou-Ntais, A. Kaloxylos, Athanasios Kanavos· 0 citations
A solver-grounded design principle is presented: a numerical result is reported only when it originates from a trusted tool and passes explicit verification, and a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency is proposed.
Daniel Rojas, Abdulwahab Albassam, Aidan G. Leung et al.· 0 citations
(English) Unlike earlier mobile generations, 6G is expected to support a wide range of applications such as immersive communications, remote healthcare, autonomous transportation, and smart cities. These use cases will significantly increase the number of connected devices and impose stringent requirements on bandwidth, latency, reliability, and energy efficiency. As a result, the networks supporting these services will face major challenges in scalability, resource management, and control. In this context, this doctoral thesis investigates the use of Multi-Agent Systems (MAS) as a foundation for next-generation network control. The goal of this thesis is to design and evaluate MAS-based solutions that improve the intelligence, scalability, and energy efficiency of optical networks across both the optical and packet layers.
The first objective addresses the optical layer by investigating centralized and distributed MAS-based approaches for dynamic spectrum control in point-to-multipoint (P2MP) connections. A centralized solution based on traffic prediction and integer linear programming computes optimal allocations under near-real-time constraints, achieving high spectrum utilization but introducing synchronization and scalability limitations. To overcome these issues, distributed architectures are proposed in which transponder agents perform decision-making locally. Three strategies are studied: a mixed-strategy gaming model, a distributed deterministic algorithm, and a multi-agent reinforcement learning (MARL) approach. The MARL solution achieves the best overall performance by anticipating traffic variations and allocating capacity proactively, while distributed methods significantly improve scalability and robustness. Communication efficiency is also studied with the MARL approach allowing for asynchronous operation and reducing inter-agent messaging. Results show that distributed MAS can approach centralized performance while avoiding bottlenecks and single points of failure.
The second objective focuses on the packet layer, where an extended MAS architecture enables end-to-end near-real-time control of network services (NS) through autonomous flow operation. Routing decisions are driven by telemetry and optimized using Deep Reinforcement Learning (DRL) to minimize delay and operational cost, while agents monitor performance and coordinate with the software-defined networking (SDN) controller. The architecture supports the full lifecycle of a NS, including deployment, dynamic reconfiguration, and handover scenarios. A model-selection approach based on offline training and real-time telemetry is proposed, together with an active probe-testing mechanism and long short-term memory (LSTM) based traffic prediction trained online by flow agents. Simulations demonstrate that transferring of trained models between agents enables accurate predictions and knowledge generation allowing for fast reconfiguration decisions while maintaining QoS over the NS.
This MAS architecture provides the foundation for the third objective where experimental results demonstrate reliable QoS maintenance and effective MAS reconfiguration during operation.
In conclusion, this thesis shows that MAS combined with learning-based decision-making, predictive analytics, and distributed control provide a flexible and effective framework for managing future networks. The proposed solutions improve scalability, adaptability, and energy efficiency while maintaining strict performance guarantees, establishing MAS as a key enabler for intelligent and autonomous 6G networks.
(Català) A diferència de les generacions mòbils anteriors, s'espera que el 6G admeti una àmplia gamma d'aplicacions com ara comunicacions immersives, atenció mèdica remota, transport autònom i ciutats intel·ligents. Aquests casos d'ús augmentaran significativament el nombre de dispositius connectats i imposaran requisits estrictes sobre l'amplada de banda, la latència, la fiabilitat i l'eficiència energètica. Com a resultat, les xarxes que donen suport a aquests serveis s'enfrontaran a grans reptes en escalabilitat, gestió de recursos i control. En aquest context, aquesta tesi doctoral investiga l'ús de sistemes multiagent (MAS) com a base per al control de xarxa de nova generació. L'objectiu d'aquesta tesi és dissenyar i avaluar solucions basades en MAS que millorin la intel·ligència, l'escalabilitat i l'eficiència energètica de les xarxes òptiques tant a la capa òptica com a la de paquets.
El primer objectiu aborda la capa òptica investigant enfocaments centralitzats i distribuïts basats en MAS per al control dinàmic de l'espectre en connexions punt a multipunt (P2MP). Una solució centralitzada basada en la predicció de trànsit i la programació lineal entera calcula assignacions òptimes sota restriccions gairebé en temps real, aconseguint una alta utilització de l'espectre però introduint limitacions de sincronització i escalabilitat. Per superar aquests problemes, es proposen arquitectures distribuïdes en què els agents transponedors prenen decisions localment. S'estudien tres estratègies: un model de joc d'estratègia mixta, un algoritme determinista distribuït i un enfocament d'aprenentatge per reforç multiagent (MARL). La solució MARL aconsegueix el millor rendiment general anticipant les variacions del trànsit i assignant la capacitat de manera proactiva, mentre que els mètodes distribuïts milloren significativament l'escalabilitat i la robustesa. També s'estudia l'eficiència de la comunicació amb l'enfocament MARL que permet el funcionament asíncron i redueix la missatgeria interagent. Els resultats mostren que el MAS distribuït pot aproximar-se al rendiment centralitzat evitant els colls d'ampolla i els punts únics de fallada.
El segon objectiu se centra en la capa de paquets, on una arquitectura MAS estesa permet el control de punta a punta en temps gairebé real dels serveis de xarxa (NS) mitjançant el funcionament autònom del flux. Les decisions d'encaminament es controlen mitjançant telemetria i s'optimitzen mitjançant l'aprenentatge per reforç profund (DRL) per minimitzar el retard i el cost operatiu, mentre que els agents supervisen el rendiment i es coordinen amb el controlador de xarxa definida per programari (SDN). L'arquitectura dóna suport al cicle de vida complet d'una xarxa de xarxa (NS), incloent-hi el desplegament, la reconfiguració dinàmica i els escenaris de traspàs. Es proposa un enfocament de selecció de models basat en l'entrenament fora de línia i la telemetria en temps real, juntament amb un mecanisme actiu de proves de sondes i una predicció de trànsit basada en memòria a curt termini (LSTM) entrenada en línia per agents de flux. Les simulacions demostren que la transferència de models entrenats entre agents permet prediccions precises i generació de coneixement que permeten prendre decisions de reconfiguració ràpides mentre es manté la QoS sobre la NS.
Aquesta arquitectura MAS proporciona la base per al tercer objectiu, on els resultats experimentals demostren un manteniment fiable de la QoS i una reconfiguració MAS eficaç durant el funcionament.
En conclusió, aquesta tesi demostra que el MAS combinat amb la presa de decisions basada en l'aprenentatge, l'anàlisi predictiva i el control distribuït proporciona un marc flexible i eficaç per a la gestió de les xarxes futures. Les solucions proposades milloren l'escalabilitat, l'adaptabilitat i l'eficiència energètica, mantenint alhora garanties de rendiment estrictes, establint el MAS com un factor clau per a les xarxes 6G intel·ligents i autònomes.
(Español) A diferencia de las generaciones móviles anteriores, se espera que 6G sea compatible con una amplia gama de aplicaciones, como las comunicaciones inmersivas, la atención médica remota, el transporte autónomo y las ciudades inteligentes. Estos casos de uso aumentarán significativamente el número de dispositivos conectados e impondrán requisitos estrictos de ancho de banda, latencia, fiabilidad y eficiencia energética. Como resultado, las redes que soportan estos servicios se enfrentarán a importantes retos de escalabilidad, gestión de recursos y control. En este contexto, esta tesis doctoral investiga el uso de Sistemas Multiagente (MAS) como base para el control de red de próxima generación. El objetivo de esta tesis es diseñar y evaluar soluciones basadas en MAS que mejoren la inteligencia, la escalabilidad y la eficiencia energética de las redes ópticas en las capas óptica y de paquetes.
El primer objetivo aborda la capa óptica mediante la investigación de enfoques centralizados y distribuidos basados en MAS para el control dinámico del espectro en conexiones punto a multipunto (P2MP). Una solución centralizada basada en la predicción de tráfico y la programación lineal entera calcula asignaciones óptimas con restricciones casi en tiempo real, logrando una alta utilización del espectro, pero introduciendo limitaciones de sincronización y escalabilidad. Para superar estos problemas, se proponen arquitecturas distribuidas en las que los agentes transpondedores toman decisiones localmente. Se estudian tres estrategias: un modelo de juego de estrategia mixta, un algoritmo determinista distribuido y un enfoque de aprendizaje por refuerzo multiagente (MARL). La solución MARL logra el mejor rendimiento general al anticipar las variaciones de tráfico y asignar capacidad de forma proactiva, mientras que los métodos distribuidos mejoran significativamente la escalabilidad y la robustez. También se estudia la eficiencia de la comunicación con el enfoque MARL, que permite la operación asíncrona y reduce la mensajería entre agentes. Los resultados muestran que el MAS distribuido puede aproximarse al rendimiento centralizado, evitando cuellos de botella y puntos únicos de fallo.
El segundo objetivo se centra en la capa de paquetes, donde una arquitectura MAS extendida permite el control integral y casi en tiempo real de los servicios de red (NS) mediante
Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform resources, ISAC design now involves many tightly coupled choices, including waveform selection, sensing algorithm setup, resource scheduling, and deployment planning. This design space is already too large to manage well through manual tuning or isolated optimizers. This article introduces the \textit{Agent Compiler}, a large language model (LLM)-enabled compilation layer that translates high-level engineering intent into complete and executable ISAC system configurations. The Agent Compiler works in four stages: intent parsing, task decomposition, policy graph synthesis, and infrastructure mapping. It produces a verifiable intermediate representation called the ISAC Policy Graph (IPG). A runtime engine then deploys the compiled configuration and supports closed-loop adaptation at three levels: fast parameter updates, partial recompilation of affected subgraphs, and full workflow recompilation. The core design principle is strict time-scale separation: the LLM handles slow-loop strategic decisions, while proven algorithms retain real-time control in the fast loop. A UAV-assisted disaster rescue example illustrates the full compilation process. We also discuss open issues, including compilation latency, output reliability, constraint verification, and pipeline security, to guide future research.
Lijie Zheng, Xudong Zhong, Baoquan Ren et al.· 0 citations