Future 6G use cases—such as AI, sensing, and immersive Extended Reality (XR)—will require stringent end-to-end communication performance. Optimizing only the network infrastructure is insufficient; coordination between network and computing resources is essential. In this paper, we propose a unified architecture that embeds a Layer 4 proxy into the User Plane Function (UPF) to control data flow and computing resources jointly. This design allows the flow to be dynamically managed based on network conditions, while simultaneously optimizing latency and throughput. The control interface is exposed via APIs, enabling applications to dynamically activate computing services and optimize data throughput for available network bandwidth. While the architecture targets future 6G networks, its feasibility is validated on a 5G Core-based prototype to emulate 6G in-network computing scenarios under realistic workloads. Our prototype evaluation demonstrates that the proposed architecture improves throughput, reduces latency and jitter, stabilizes data flow, and enhances AI-based video object detection performance.

Computing and networking platforms would become more decentralized toward B5G and 6G. Various functions and services will be deployed at the far edge, a site located physically closer to the user devices than the current edge-cloud design pattern in 5 G MEC. In such ultra-distributed environments, efficient use of those finite and limited computing and networking resources is one of the most important requirements for telecom operators. UPF, one component of 5GC, is a key function that links mobile and service domains. UPF is a typical example deployed in various platforms, from far edge to on-premise central data centers and clouds. DPUs are an advanced form of SmartNICs that are good at network, security, and storage communication processing and can be controlled as one of the in-network computing infrastructures. In this paper, we present the future telecom network architecture by offloading various network functions to DPUs and implementing UPF features as a first target. We also identify the feasibility of scalable UPF configurations from small to large scale leveraging DPUs and the DPU-dependent implementation issues for commercial applications by conducting essential functionality evaluation and carrier-grade large-scale performance tests.

Since cloud technology is rapidly advancing and spreading, is playing an increasingly important role in society, and will be widely used as the foundation for mobile network systems, innovative concepts called in-network computing (INC), in which computing functions are deployed, are widely considered for network and service functions. One of the benefits provided by INC is the optimization of computing and networking processing stacks. In this paper, we propose a system architecture for a mobile network equipped with computing capabilities called the Innetwork Service Acceleration Platform (ISAP). This enables end-to-end service performance acceleration, realizing processing optimization. We also present a prototype implementation of ISAP that integrates network and computing functions into a shared data plane on the basis of our proposed architecture, which includes a 3GPP standard-compliant 5G core network, server-less orchestration mechanisms, and multiple accelerator cards. Evaluations, including simulation and performance measurements, show ISAP has advantages over conventional designs, such as improved resource utilization efficiency, low latency, and deterministic service and network processing. These results validate our proposed scheme for realizing 6G INC.

Since cloud technology will permeate mobile networks in 6G, scenarios in which computing functions are deployed in the network are widely considered for not only network functions but also service functions. In this paper, we propose a system architecture, a mobile network equipped with computing capabilities, which enables end-to-end service performance acceleration. We evaluated our proposed architecture through evaluations and observed advantages over conventional designs, such as improved resource utilization efficiency and extremely low-latency service and network processing. The prototype implementation of integrating network and computing functions on the basis of our proposed architecture is also presented, which includes a 3GPP standard-compliant 5G core network, serverless orchestration mechanisms, and multiple accelerator cards. With the prototype above, we demonstrate how our proposed architecture federates network and service functions control and how it accelerates the application processing performance.

We propose a scalable regression model with spatially and temporally varying coefficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and temporal locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation–maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.

In recent years, the number of vacant houses in Japan has continued to increase throughout the country, and understanding their distribution is an important problem for local governments. However, the method of surveying the distribution of vacant houses is primarily based on visual inspection from the outside, which requires much time, labor, and a budget for the survey. In this study, we developed a method to rapidly estimate the distribution of vacant houses in a municipality by developing a database of vacant houses in Maebashi City, Gunma Prefecture, a typical local city of Japan, by integrating a digital housing map and pinpoint data (basic resident register, water usage, and fixed asset taxation register) owned by the municipality, and performing machine learning using actual information on the distribution of vacant houses as ground truth data. We developed a method to rapidly estimate the distribution of vacant houses over the entire municipality.

The orchestration and management of 5G network slicing (NS) requires cross-domain orchestration across 5G radio access network (RAN), 5G core network (CN), and transport network and also coordination of closed loops of each 5G network segment. We propose a resource orchestration and management architecture for end-to-end (E2E) 5G network slices to automate the flexible and high-performance NS’s management and orchestration. Through developing a prototype of the proposed architecture on an experimental 5G network, the effectiveness of our architecture is proven through prominent use cases certified by ETSI ZSM.

Network functions that apply advanced cloud-native technologies are called Cloud-native Network Functions (CNFs). CNFs reap many of the benefits of a microservices architecture. However, CNFs are expected to be used, for example, as a platform for MEC and will require more distributed deployment in various cloud environments from the edge to the private or public cloud than ordinary web services. As a result, the number of combinations of software and hardware resources will explode, making it difficult to design optimal hardware resources in accordance with the requirements of the various network services. To overcome this challenge, we propose an automated CNF provisioning engine that optimizes the hardware resources allocated to CNFs from the viewpoint of performance assurance and minimizing equipment costs even in various clouds. In this paper, we used machine learning for the cloud-native 5G mobile core to build a performance prediction model for both control plane and user plane functions under various hardware conditions on the basis of the performance characteristics data obtained from our test platform. From evaluating the prediction accuracy of the constructed models, we clarify that the models can predict with high accuracy even using features that can be easily fed back to the hardware resource design.

The decomposition of a 5G radio access network (RAN) accelerates inter-operator infrastructure sharing of the corresponding transport network as well as the centralized deployment of a centralized unit (CU) and distributed unit (DU). We propose an architecture that enables inter-operator infrastructure sharing of 5G xHaul as slices and on-demand creation of xHaul network slices dynamically over multiple network operators that are interconnected and orchestrated with the standardized interface of the Open Networking Foundation (ONF) Transport API (TAPI) and corresponding standard from Metro Ethernet Forum (MEF). Through a proof of concept, we validated the implementation of the architecture in three live network domains for 5G xHaul.

The signaling traffic in mobile networks has been increasing rapidly due to the emergence of various types of devices such as smartphones and machine-to-machine (M2M) devices. This signaling traffic heavily burdens network controllers and could trigger network failures. Therefore, the need of a scalable device management method for reducing signaling traffic volume is increasing and the standardization activities have begun in 3GPP. The current mobile networks treat all devices as smartphones and frequently control the devices in a complicated way to provide advanced user experiences, such as seamless handover and longer battery life. This is a major cause of a huge amount of signaling traffic. However, M2M devices have different characteristics from those of smartphones, such as low-mobility, small amount data, low-frequency, and no power constraints. Therefore, a network does not require such a complicated management for M2M devices, and there is room for simplifying conventional control procedures. Moreover, smartphones often act like M2M devices (e.g. a smartphone is stationary when a user is in his/her home) and we believe a characteristic change aware device management method is necessary for further signaling traffic reduction. To achieve the signaling volume reduction, we propose 3GPP network parameter optimization approach in conjunction with 3GPP architectural enhancements. We evaluated the feasibility of the proposed method with a network simulator.

The accommodation of machine-to-machine (M2M) terminals in mobile networks is important; therefore, future network architecture supporting M2M services is of intense interest to mobile network operators. We propose an implemental architecture of a virtualized evolved packet core (vEPC) to accommodate M2M services. The proposed architecture deploys dedicated vEPCs based on the functional requirements of services. Every vEPC is optimized by eliminating EPC components or replacing standardized interface protocols with internal application interworking. We confirm the validity of the proposed architecture by experimentally evaluating CPU resource consumption. We also confirm that the proposed architecture reduces CPU time consumption by up to 27% by reducing signaling message volume, and improved performance is observed independently with M2M terminal mobility or communication characteristics.

To offer Web-IMS convergent services, network operators are providing interfaces for Web servers such as Parlay X on top of IMS application servers. However, this type of architecture leads to the building of at least as many interfaces as there are IMS services opened to the Web servers. This architecture also lacks consideration on media/transport processing. This paper presents a unique and generic service-independent functional entity on the IMS network border named Web Session Controller to handle interactions both at the signaling level and at the media/transport level to control Web-IMS communications. A prototype has been developed and is described that allows new types of services to be offered, e.g., push customized Web content to IMS users.