2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6403-6418· 0 citations· 58 references
TL;DR
A predictive QoE-driven RRM framework built upon an AI-enabled Network Digital Twin, which operates as a high-fidelity replica of the physical network to support proactive and efficient system-level resource allocation, is proposed.
Abstract
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.
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.
Yukai Wang, Janghee Woo, G. Hahm et al.· International Conference on...· 0 citations
Achieving deterministic latency for time-sensitive flows within integrated 5G and Time-Sensitive Networking (TSN) ecosystem requires the active mitigation of stochastic delays inherent in 5G New Radio (NR). While existing research typically relies on pessimistic guard bands or over-provisioned time-domain resources via wired TSN mechanisms, these approaches fail to adaptively reserve NR resources under dynamic channel conditions to suppress Packet Delay Variation (PDV). This work addresses this gap by proposing a joint NR MAC scheduling and Link Adaptation (LA) framework. We introduce Link Adaptive Semi-Persistent Scheduling (LA-SPS), a framework that ensures cycle-synchronous uplink opportunities by dynamically reconfiguring resource budgets and modulation parameters from real-time channel feedback. To manage the combinatorial complexity of joint resource allocation, we employ a Graph Neural Network (GNN) to encode scalable network states and Proximal Policy Optimization (PPO) for stable, real-time decision-making. This modular framework functions as a radio-side control loop designed for seamless coupling with end-to-end Time-Aware Shaper (TAS) scheduler, enabling a fully co-adaptive industrial network.
Syed Tasnimul Islam, José Fontalvo-Hernández· International Conference on...· 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
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
Efficient coexistence of eMBB and URLLC services remains a critical challenge in AI-native Radio Access Networks (RANs). This paper proposes a two-timescale Hierarchical Reward Weighting (HRW) framework based on multiobjective reinforcement learning for context-aware O-RAN slicing under a Constrained Markov Decision Process (CMDP) formulation. The proposed architecture separates long-term policy adaptation from fast-timescale radio scheduling, mitigating the non-stationarity inherent in multiobjective RAN optimization. At the slow layer, a non-realtime RIC rApp exploits a long-term network context and a differentiable Softmax mapping to adapt slice reward preferences. These policies are propagated through the $O$ -RAN control hierarchy to guide downstream scheduling decisions. At the fast layer, decentralized scheduling agents embedded within the Open Distributed Unit (O-DU) MAC layer execute sub-millisecond Physical Resource Block (PRB) allocation and packet preemption, avoiding near-RT RIC transport latency constraints. Evaluated under a multiuser MIMO-OFDMA environment, the proposed framework improves resource utilization by up to 60.8% over static partitioning while maintaining bounded URLLC tail-latency behavior and strict Service Level Agreement (SLA) compliance. The results demonstrate the feasibility of AI-native hierarchical O-RAN control and align with the ITU-T visions for autonomous 6G RAN intelligence.
Charles Ssengonzi, Okuthe P. Kogeda, T. Olwal· 2026 ITU Kaleidoscope - AI a...· 0 citations
A deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN and indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization.