As IoT services become more popular, mobile networks will have to accommodate a wide variety of devices that have different requirements such as different bandwidth limitations and latencies. This paper describes edge distributed mobile network architectures for the IoT era based on dedicated network technology and multi-access edge computing technology, which have been discussed in 3GPP and ETSI. Furthermore, it describes two context-aware control methods that will make mobile networks on the network architecture more efficient, reliable, and real-time: autonomous and distributed mobility management and bandwidth-guaranteed transmission rate control in a networked control system.

Ultrafast MR imaging (e.g., echo-planar imaging) acquires all the data within only several tens of milliseconds. This method, however, is affected by static magnetic field inhomogeneities and chemical shift; therefore, a high degree of field homogeneity and water and fat signal separation are required. However, it is practically impossible to obtain an homogeneous field within a subject even if in vivo shimming has been performed. In this paper, we describe a new ultrafast MR imaging method called Ultrafast Single-shot water and fat Separated Imaging (USSI) and a correction method for field inhomogeneities and chemical shift. The magnetic field distribution whthin the subject is measured before thd scan and used to obtain images without field inhomogeneity distortions. Computer simulation results have shown that USSI and the correction method can obtain water and fat separated images as real and imaginary parts, respectively, of a complex Fourier transform with a single-shot scan. Image quality is maintained in the presence of field inhomogeneities of several ppm similar to those occurring under practical imaging conditions. Limitations of the correction method are also discussed.

Network service providers need to appropriately design systems and carefully configuring the settings and parameters to ensure that the systems keep running consistently and deliver the desired services. This can be a heavy and error-prone task. Intent-based system design methods have been developed to help with such tasks. These methods receive service-level requirements and generate service configurations to fulfill the given requirements. One such method is search-based system design, which can flexibly generate systems of various architectures. However, it has difficulty dealing with constraints on the quantitative parameters of systems, e.g., disk volume, RAM size, and QoS. To deal with practical cases, intent-based system design engines need to be able to handle quantitative parameters and constraints. In this work, we propose a new intent-based system design method based on search-based design that augments search states with quantitative constraints. Our method can generate a system that meets both functional and quantitative service requirements by combining a search-based design method with constraint checking. Experimental results show that our method can automatically generate a system that fulfills all given requirements within a reasonable computation time.

Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.

Traffic collision is an extremely serious issue in the world today. The World Health Organization (WHO) reported the number of road traffic deaths globally has plateaued at 1.25 million a year. In an attempt to decrease the occurrence of such traffic collisions, various driving systems for detecting pedestrians and vehicles have been proposed, but they are inadequate as they cannot detect vehicles and pedestrians in blind places such as sharp bends and blind intersections. Therefore, mobile networks such as long term evolution (LTE), LTE-Advanced, and 5G networks are attracting a great deal of attention as platforms for connected car services. Such platforms enable individual devices such as vehicles, drones, and sensors to exchange real-time information (e.g., location information) with each other. To guarantee effective connected car services, it is important to deliver a data block within a certain maximum tolerable delay (called a deadline in this work). The Third Generation Partnership Project (3GPP) stipulates that this deadline be 100 ms and that the arrival ratio within the deadline be 0.95. We investigated an intersection at which vehicle collisions often occur to evaluate a realistic environment and found that schedulers such as proportional fairness (PF) and payload-size and deadline-aware (PayDA) cannot satisfy the deadline and arrival ratio within the deadline, especially as network loads increase. They fail because they do not consider three key elements — radio quality, chunk size, and the deadline — when radio resources are allocated. In this paper, we propose a deadline-aware scheduling scheme that considers chunk size and the deadline in addition to radio quality and uses them to prioritize users in order to meet the deadline. The results of a simulation on ns-3 showed that the proposed method can achieve approximately four times the number of vehicles satisfying network requirements compared to PayDA.

We present detailed measurements and analysis of the guided acoustic wave Brillouin scattering (GAWBS)-induced depolarization noise in a multi-core fiber (MCF) used for a digital coherent optical transmission. We first describe the GAWBS-induced depolarization noise in an uncoupled four-core fiber (4CF) with a 125μm cladding and compare the depolarization noise spectrum with that of a standard single-mode fiber (SSMF). We found that off-center cores in the 4CF are dominantly affected by higher-order TRn,m modes rather than the TR2,m mode unlike in the center core, and the total power of the depolarization noise in the 4CF was almost the same as that in the SSMF. We also report measurement results for the GAWBS-induced depolarization noise in an uncoupled 19-core fiber with a 240μm cladding. The results indicate that the amounts of depolarization noise generated in the cores are almost identical. Finally, we evaluate the influence of GAWBS-induced polarization crosstalk (XT) on a coherent QAM transmission. We found that the XT limits the achievable multiplicity of the QAM signal to 64 in a transoceanic transmission with an MCF.