Performance Analysis of Priority-Aware DRL-based Call Admission Control for 5G Network

Comprehensive Review of Optimization Techniques for User-Centric Distributed Network Slicing in 5G Networks

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.

Implementation of an Admission Control in SDN Network

SLA-AWare RAN Slicing Via Online Meta-Learning

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.

Congestion-Aware Mobility Robustness Optimization: A Multi-Vendor Approach in Live Networks

Mobility Robustness Optimization (MRO) is essential to maintain service continuity, yet its execution often conflicts with Mobility Load Balancing (MLB) functions. While existing literature relies heavily on simulated environments, this paper presents a novel, data-driven, and congestion-aware MRO framework design for live networks. The proposed approach employs multi-vendor capabilities and a five-phase rule-based system to ensure that Handover Control Parameter (HCP) adjustments are grounded in domain-specific knowledge. The framework was deployed in a live network, processing data from over 10,000 cells. Live results show a 19.9% increase in user throughput, a 7.6% decrease in Handover Failures (HOF), and a 36.6% reduction in packet drop rate. Ultimately, this study demonstrates that the proposed framework is highly scalable and adaptive for diverse multi-vendor network environments.

Data-Driven Online Slice Admission Control and Resource Allocation in NextG Mobile Networks

This work introduces Online Pricing-based Slice Admission Control and Resource Allocation (OPA) framework, which dynamically assigns pseudo-prices to resources that capture long-term scarcity and anticipated inter-temporal opportunity costs and designs an exponential pricing strategy that guarantees bounded worst-case performance.

End-to-end delay performance analysis of the 5G-TSN network using network calculus

An improved strict-priority Deficit Round-Robin (SP-DRR) scheduling strategy is proposed and incorporates it into a unified moment generating function (MGF) analytical framework, referred to as SP-DRR-MGF, for probabilistic E2E delay analysis in 5G–TSN networks.