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

Abstract

Fifth generation (5G) networks deliver multi-gigabit data rates, sub-millisecond latency, and dense connectivity through a customised service delivery paradigm built on virtualisation and network slicing (NS). However, conventional NS frameworks rely on threshold-based control and lack the context awareness needed for autonomous, user-centric decision-making. Machine learning (ML) optimization-driven methods such as deep reinforcement learning (DRL) and hybrid metaheuristic–ML approaches can close this gap by inferring user bandwidth behaviour, anticipating congestion, and enacting proactive corrective actions. This paper presents a systematic, PRISMA-based review of 2024–2026 ML-based optimization for user-centric distributed NS, screening 6,286 records to 67 core studies that are normalised through a common evidence tuple. A comprehensive critical review is then presented with role-oriented matrices spanning admission control, resource allocation and offloading, orchestration, graph learning, and federated learning (FL). From this synthesis, we derive recurring optimization formulations and identify persistent gaps, namely the absence of direct Quality of Experience (QoE) inference, privacy preservation, topology awareness, and validated deployment. To address these gaps, we propose a QoE-aware framework for Multi-Access Edge Computing (MEC)-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. The proposed framework is also grounded in preliminary validation from our two prior slice admission control and load balancing studies, offering a scalable, privacy-aware, and truly user-centric path towards 5G and Beyond 5G networks.