2026· IEEE Transactions on Communications· Vol 74, pp. 12303-12319· 0 citations· 46 references
Abstract
The coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB) can simultaneously meet the reliability and real-time performance requirements of critical services as well as the requirements of high-bandwidth services. In 5G and beyond 5G (B5G) networks, radio access network (RAN) slicing can ensure the differentiated quality of service (QoS) for coexisting heterogeneous services. This paper investigates a dynamic resource allocation framework, aiming to maximize resource utilization subject to QoS constraints. The optimization problem is an NP-hard integer program. To address this issue, we propose a hierarchical proximal policy optimization (PPO) model assisted by a traffic prediction algorithm, namely graph-aggregated Extended Long Short-Term Memory (GxLSTM), which decouples the resource allocation behavior between eMBB and URLLC. Transfer learning is introduced to improve the learning efficiency, forming a Transfer Learning-assisted Hierarchical PPO algorithm (TL-HPPO). In addition, considering the lightweight algorithm deployment requirements for edge networks, based on angular knowledge distillation (AKD), GxLSTM and TL-HPPO are respectively distilled to obtain AKD-traffic prediction (AKD-TP) and AKD-resource allocation (AKD-RA), thereby reducing computational complexity. Simulation results show that the proposed algorithms outperform comparative algorithms while ensuring QoS, effectively improving resource utilization.
The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators'profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.
Mayank Ramnani, Shasank Dixit, Sushil K. Yadav et al.· 0 citations
Real-time inter-slice resource allocation in the Radio Access Network (RAN) is a critical control function in 5G and emerging 6G networks, where the scheduler in the Distributed Unit (DU) dynamically allocates physical resources, namely Physical Resource Blocks (PRBs), to different network slices to meet their diverse Quality of Service (QoS) requirements. To address the need for faster and more flexible radio resource management, and inspired by recent efforts to extend the O-RAN architecture with a real-time controller, we investigate slice-level PRB allocation through the lens of online learning. We formulate inter-slice scheduling as a dynamic decision problem and develop a system model that captures per-slice Service Level Agreement (SLA) requirements and throughput variations over configurable time windows, without assuming future channel knowledge. Our scheduling solution is implemented as a real-time RAN control application, in line with the O-RAN proposition for dApps that are programmable and distributed software components for fine-grained control in O-RAN DUs (O-DUs) and Centralized Units (O-CUs). The proposed approach adapts inter-slice radio resource allocations based on telemetry, with low computational complexity. Experimental results show sublinear dynamic regret, up to 85% fewer SLA violations than static baselines, and submillisecond amortized control overhead. Overall, these findings highlight dynamic-benchmark online control as a practical mechanism for real-time, SLA-aware slicing in O-RAN.
Asim Zoulkarni, C. Papagianni, Georgios Iosifidis et al.· International Mediterranean...· 0 citations
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yichen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 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
As wireless networks transition toward the 6G era, supporting strictly heterogeneous services such as eMBB, URLLC, and mMTC over a unified infrastructure becomes a fundamental challenge. Traditional Radio Access Network (RAN) slicing often relies on upper-layer logical abstractions, which fail to address physical inter-slice interference and the boundary effect of cellular architectures. This paper proposes a novel Profile-Aware Hierarchical RAN Slicing Framework for User-Centric Cell-Free Massive MIMO systems to overcome these limitations. The proposed framework comprises four hierarchical stages: utility-based profile-aware clustering, dynamic inter-slice resource partitioning for power and bandwidth, hybrid central processing unit-to-access point power budgeting, and real-time power allocation using a Bipartite Graph Convolutional Network (BiGCN). By incorporating service-specific requirements into physical layer resource management, the framework ensures strict quality-of-service isolation and global energy efficiency. Simulation results demonstrate that the proposed integrated framework achieves a high Jain’s Fairness Index, exceeding 0.95 for most profiles and provides up to a 48-fold energy efficiency improvement for battery-constrained devices compared to non-slicing baselines. Furthermore, the BiGCN-based allocation module attains near-optimal performance with millisecond-level inference latency, confirming its feasibility for mission-critical real-time applications. This comprehensive approach effectively eliminates the trade-off between aggregate throughput and individual reliability, providing a scalable solution for next-generation sliced networks.
Ja-Eun Kim, Hye-Yoon Jeong, Ji-Woo Lee et al.· IEEE Access· 0 citations
The proliferation of heterogeneous traffic with divergent Quality of Service (QoS) requirements in 5G/6G networks necessitates fundamentally new resource allocation strategies. The existing deep reinforcement learning methods suffer from high training complexity, while heuristic approaches lack adaptability. We propose COATI (Chaotic Optimization Algorithm with Traffic Intelligence), a metaheuristic framework that synergizes the Coati Optimization Algorithm (COA) with chaotic dynamics and traffic-aware operators. Formulated as a mixed integer nonlinear program (MINLP), COATI introduces: (i) logistic map-based chaotic initialization for diversity preservation, (ii) adaptive Lévy flights guided by buffer status reports for dynamic exploration–exploitation trade-offs, and (iii) a QoS-aware fitness function incorporating proportional fairness constraints. Simulations based on 3GPP scenarios demonstrate that COATI reduces URLLC latency violations by 38%, improves eMBB throughput by 9.2%, and converges 27% faster than state-of-the-art methods. The algorithm’s bio-inspired yet mathematically rigorous approach establishes a new paradigm for real-time resource management in dense cellular networks.
Nandini Maninarayana, G. S. Satapathi, K. S. Karthik et al.· Telecommunications Systems· 0 citations