As Fifth Generation Networks (5G) networks evolve, operators have an opportunity to create high-quality customer experiences by ensuring seamless and personalized connectivity through consumer-oriented network slicing. However, effectively managing consumer slices remains a challenge due to dynamic user demands, mobility patterns, and the need for real-time Quality of Service (QoS) assurance. This paper presents an Artificial Intelligence (AI)-driven framework for intelligent service feasibility, qualification, and provisioning in 5G networks, to automate consumer slicing. By leveraging real-time data from the Radio Access Network (RAN) and Core Network (CN), along with Service Quality Indicator (SQI), the framework enables dynamic resource allocation, predictive service qualification, and proactive slice optimization. Key innovations of the frame-work include the coherent integration of AI-driven slice load prediction, mobility-aware service provisioning, and automated QoS assurance via Application Programming Interfaces (APIs), ensuring optimal performance for consumer applications while maintaining compliance with Service Level Agreement (SLA). Experimental results demonstrate the framework’s capabilities in enhancing user experiences by enabling personalized slice selection, reducing service disruptions, and optimizing network resource utilization.

Networked systems have recently aimed to use multiple access networks in parallel to increase resiliency, availability and capacity. However, different paths may have different latency characteristics, which may lead to out-of-order packet delivery. This may severely impact both the end-to-end application performance and the capacity utilisation of multiaccess systems. In this paper, we show that in-network support for packet reordering for multiaccess systems that are based on multiple transport layer tunnels is beneficial for several application types. Our findings are applicable to TCP and QUIC traffic in the 3GPP ATSSS context, where we use the MP-DCCP tunneling framework with a buffer-based packet reordering approach that uses a dynamic timing threshold to cope with variation of path delays over time. We demonstrate achievable performance gains for a wide range of path latency differences and end-to-end round trip times when using different in-network reordering algorithms.