The 5thGeneration (5G) mobile network, being standardized in 3GPP, is a convergence network that supports both 3GPP Radio Access Technology (RAT) and a non-3GPP RAT. The 3GPP RAT is a new RAT to provide much increased data rate for enhanced mobile broadband service. Wi-Fi and fixed wireline are mainly considered for non-3GPP access technologies but we only discuss Wi-Fi as a non-3GPP RAT for the sake of simplicity. This paper discusses a multi-access packet data unit (PDU) session, whose traffic is sent over 3GPP access, or over non-3GPP access, or over both accesses. 3GPP specification group 2 (SA2) is studying technical problems and solutions for this issue with a name of Access Traffic Steering, Switching and Splitting (ATSSS). ATSSS is expected to enrich user experience by distributing user traffic across 3GPP RAT and non-3GPP access technologies. This paper shows an implementation of multiple-access PDU session (MAPS) to support ATSSS in 5G mobile network. We briefly introduce ATSSS concept and discuss design issues of ATSSS in our 5G trial system. The design issues focus on minimal modification to the existing signaling procedures in the architecture of the 5G system with a single-access PDU session. We designed legs on each access to extend a session, which are managed together as a single PDU session.

We present a type system and inference algorithm for a rich subset of JavaScript equipped with objects, structural subtyping, prototype inheritance, and first-class methods. The type system supports abstract and recursive objects, and is expressive enough to accommodate several standard benchmarks with only minor workarounds. The invariants enforced by the types enable an ahead-of-time compiler to carry out optimizations typically beyond the reach of static compilers for dynamic languages. Unlike previous inference techniques for prototype inheritance, our algorithm uses a combination of lower and upper bound propagation to infer types and discover type errors in all code, including uninvoked functions. The inference is expressed in a simple constraint language, designed to leverage off-the-shelf fixed point solvers. We prove soundness for both the type system and inference algorithm. An experimental evaluation showed that the inference is powerful, handling the aforementioned benchmarks with no manual type annotation, and that the inferred types enable effective static compilation.

In order to provide context-aware services, context modeling is one of the most important tasks. Ontology has been widely accepted for the context modeling but it brings some overheads in the process of reasoning large-scale and dynamic information. In mobile networks, especially, ontology should be verified to be applicable for Context-aware Services since real-time responsiveness for large scale moving users is highly required to be guaranteed. This paper proposes an efficient context model to overcome the limitation of ontology. For the purpose, we design a conceptual context model and compare the processing time of the context-aware mobile service system implying the three different context modeling approaches ontology, database, and proposed hybrid modeling. As the result of performance evaluations, the proposed hybrid model shows faster processing time among them and lesser affected by the major metrics of mobile services changeability and number of users than the two existing models. Based on the experimental results, we expect that the proposed hybrid model is very useful and efficient for fast responsiveness to support Context-aware Services for large scale users in mobile networks.