Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1087-1089· 0 citations· 9 references
Abstract
Efficient long-term network evolution is becoming increasingly critical in dense 5G-Advanced and beyond cellular systems, where persistent traffic imbalances and localized congestion pose significant challenges that conventional short-term radio resource management alone cannot fully mitigate. This paper proposes a digital twin (DT)-enabled non-real-time (NRT) network evolution framework integrated with a large language model (LLM). Within this architecture, the digital twin provides a high-fidelity, controllable environment for evaluating infrastructure actions, while the LLM serves as a strategic orchestration engine that recommends cost-efficient network upgrades based on observed network states. Unlike traditional optimization methods that require exhaustive mathematical reformulations for each specific scenario, the proposed framework leverages the reasoning capabilities of LLMs to interpret operator objectives and constraints in natural language, generating structured evolution plans. The considered NRT action space encompasses antenna upgrades, bandwidth expansion, and new base station (BS) deployment. A techno-economic formulation is introduced to jointly evaluate load reduction performance and overall economic expenditure. Numerical results in a dense cellular scenario demonstrate that the framework effectively reduces peak resource utilization and provides diverse, coordinated evolution strategies tailored to varying network conditions.
The evolution toward Beyond 5G networks introduces stringent requirements for intelligent Radio Resource Management (RRM) capable of jointly optimizing Quality of Experience (QoE) and resource utilization under highly dynamic conditions. This paper proposes a predictive QoE-driven RRM framework built upon an AI-enabled Network Digital Twin (NDT), which operates as a high-fidelity replica of the physical network to support proactive and efficient system-level resource allocation. The proposed approach integrates a Deep Learning (DL)-based module for forecasting future objective QoE metrics, namely Mean Opinion Score (MOS) values, with a Deep Reinforcement Learning (DRL) agent for dynamic Physical Resource Block (PRB) allocation. By incorporating predicted QoE levels over a finite horizon into the DRL agent’s state representation, the framework enables foresighted and policy-aware resource management while reducing synchronization overhead between the NDT and the physical infrastructure. Extensive simulations under heterogeneous traffic loads and QoE policies demonstrate that the proposed approach maintains high median QoE levels while adaptively regulating resource utilization, avoiding the systematic saturation observed with baseline static schedulers. The results further highlight stable learning behavior across DRL variants and confirm real-time feasibility with limited computational overhead.
Enrico Boffetti, Arcangela Rago, G. Piro et al.· IEEE Transactions on Network...· 0 citations
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
Charalampos Oikonomidis, E. T. Michailidis, N. Miridakis· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
As Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enabled networks scale in size and complexity, monitoring and managing Service Function Chains (SFCs) under stringent latency and resource constraints becomes increasingly challenging. Although Deep Reinforcement Learning (DRL) is widely applied to SFC provisioning and Virtual Network Function (VNF) placement, enhanced network state monitoring is crucial to capture unexpected network conditions and guide DRL agents toward more adaptive decisions. In this context, Language Models (LMs) enable flexible, natural-language (NL)–based, query-driven network monitoring; however, directly processing complex multi-metric NL queries is computationally expensive and error-prone. This paper proposes an end-to-end (E2E) edge-based query translation pipeline that decomposes multi-metric NL queries into simpler single-metric sub-queries. Query decomposition is performed using a retrieval-augmented language model (RAG-LLM) and compared with a lightweight rule-based decomposition baseline. The resulting sub-queries are translated into Structured Query Language (SQL) using FLAN-T5. A cloud-only baseline, which directly translates NL queries to SQL without decomposition, is also evaluated. The results show that the rule-based edge pipeline achieves the lowest latency, reducing E2E latency by up to 78% compared to RAG-LLM and 18% compared to cloud execution under high workloads. Under increasing arrival rates for the largest workload, the rule-based edge pipeline maintains superior performance over cloud, reducing total E2E latency by 57% at $\lambda = 0.8$ . While RAG-LLM provides greater flexibility for unseen query patterns, both edge-based approaches achieve 100% NL2SQL accuracy with zero decomposition failures, outperforming the cloud-only baseline (95% accuracy).
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan et al.· IEEE Transactions on Network...· 0 citations
This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems and investigates the application of advanced machine learning paradigms specifically deep reinforcement learning, federated learning, and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement.
N. P J, Jeeva Jothi· International Journal of Com...· 0 citations
We introduce an open live multi-technology cellular network dataset derived from real, operational base stations of a commercial mobile operator in Slovakia. It provides a rare look inside live 2G, 4G and 5G Radio Access Networks (RANs) through Performance Management (PM) counters collected at 15-minute intervals and mapped to cell-level granularity. Each baseband processes multiple radio configurations, enabling multi-band and multi-generation correlation studies across real network deployments. Unlike any previously released resource, this dataset exposes real energy consumption values alongside radio and user-level performance indicators such as utilisation, Channel Quality Indicator (CQI), number of Radio Resource Control (RRC) connected and active users, uplink and downlink data volume and Multiple-Input Multiple-Output (MIMO) rank for all network cells where applicable. It supports research in energy-aware RAN optimisation, data-driven network management and cross-layer performance modelling. The dataset has been validated for internal consistency and is released under the Creative Commons Attribution (CC BY) license to foster reproducible and open telecommunications research.
Peter Lehoczký, Matúš Turcsány, L. Krajčovičová et al.· Scientific Data· 0 citations