This paper shows that upper bounds on the coefficients of the shortest vector of a lattice can be represented using the smallest eigenvalue of the Gram matrix for the lattice, obtains its distribution for high-dimensional random Goldstein-Mayer lattice, and applies it to determine the percentage of zeros of coefficient vector.

For an [n, k, d] (r, δ)-locally repairable codes ((r, δ)-LRCs), its minimum distance d satisfies the Singleton-like bound. The construction of optimal (r, δ)-LRC, attaining this Singleton-like bound, is an important research problem in recent years for thier applications in distributed storage systems. In this letter, we use Reed-Solomon codes to construct two classes of optimal (r, δ)-LRCs. The optimal LRCs are given by the evaluations of multiple polynomials of degree at most r - 1 at some points in Fq. The first class gives the [(r + δ - 1)t, rt - s, δ + s] optimal (r, δ)-LRC over Fq provided that r + δ + s - 1≤q, s≤δ, s

Recently, there has been a surge of interest in Artificial Intelligence (AI) and its applications have been considered in various fields. Mobile networks are becoming an indispensable part of our society, and are considered as one of the promising applications of AI. In the Beyond 5G/6G era, AI will continue to penetrate networks and AI will become an integral part of mobile networks. This paper provides an overview of the collaborations between networks and AI from two categories, “AI for Network” and “Network for AI,” and predicts mobile networks in the B5G/6G era. It is expected that the future mobile network will be an integrated infrastructure, which will not only be a mere application of AI, but also provide as the process infrastructure for AI applications. This integration requires a driving application, and the network operation is one of the leading candidates. Furthermore, the paper describes the latest research and standardization trends in the autonomous networks, which aims to fully automate network operation, as a future network operation concept with AI, and discusses research issues in the future mobile networks.

The significance of individuals' location information has been increasing recently, and the utilization of such data has become indispensable for businesses and society. The possible uses of location information include personalized services (maps, restaurant searches and weather forecast services) and business decisions (deciding where to open a store). However, considering that the data could be exploited, users should add random noise using their terminals before providing location data to collectors. In numerous instances, the level of privacy protection a user requires depends on their location. Therefore, in our framework, we assume that users can specify different privacy protection requirements for each location utilizing the adversarial error (AE), and the system computes a mechanism to satisfy these requirements. To guarantee some utility for data analysis, the maximum error in outputting the location should also be output. In most privacy frameworks, the mechanism for adding random noise is public; however, in this problem setting, the privacy protection requirements and the mechanism must be confidential because this information includes sensitive information. We propose two mechanisms to address privacy personalization. The first mechanism is the individual exponential mechanism, which uses the exponential mechanism in the differential privacy framework. However, in the individual exponential mechanism, the maximum error for each output can be used to narrow down candidates of the actual location by observing outputs from the same location multiple times. The second mechanism improves on this deficiency and is called the donut mechanism, which uniformly outputs a random location near the location where the distance from the user's actual location is at the user-specified AE distance. Considering the potential attacks against the idea of donut mechanism that utilize the maximum error, we extended the mechanism to counter these attacks. We compare these two mechanisms by experiments using maps constructed from artificial and real world data.

This paper describes the architectural design, services, and operation and monitoring functions of Science Information NETwork 6 (SINET6), a 400-Gigabit Ethernet-based academic backbone network launched on a nationwide scale in April 2022. In response to the requirements from universities and research institutions, SINET upgraded its world-class network speed, improved its accessibility, enhanced services and security, incorporated 5G mobile functions, and strengthened international connectivity. With fully-meshed connectivity and fast rerouting, it attains nationwide high performance and high reliability. The evaluation results of network performance are also reported.

It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.

Zero Trust Networking (ZTN) is a security model where no default trust is given to entities in a network infrastructure. The first bastion of security for achieving ZTN is strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A third party can intercept a JWT, and the payload information can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT. The blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides enrollment functionality. At the same time, our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

Beyond 5G is the next generation mobile communication system expected to be used from around 2030. Services in the 2030s will be composed of multiple systems provided by not only the conventional networking industry but also a wide range of industries. However, the current mobile communication system architecture is designed with a focus on networking performance and not oriented to accommodate and optimize potential systems including service management and applications, though total resource optimizations and service level performance enhancement among the systems are required. In this paper, a new concept of the Beyond 5G cross-industry service platform (B5G-XISP) is presented on which multiple systems from different industries are appropriately organized and optimized for service providers. Then, an architecture of the B5G-XISP is proposed based on requirements revealed from issues of current mobile communication systems. The proposed architecture is compared with other architectures along with use cases of an assumed future supply chain business.

We have developed an all-optical fiber link antenna measurement system for a millimeter wave 5th generation mobile communication frequency band around 28 GHz. Our developed system consists of an optical fiber link an electrical signal transmission system, an antenna-coupled-electrode electric-field (EO) sensor system for 28GHz-band as an electrical signal receiving system, and a 6-axis vertically articulated robot with an arm length of 1m. Our developed optical fiber link electrical signal transmission system can transmit the electrical signal of more than 40GHz with more than -30dBm output level. Our developed EO sensor can receive the electrical signal from 27GHz to 30GHz. In addition, we have estimated a far field antenna factor of the EO sensor system for the 28GHz-band using an amplitude center modified antenna factor estimation equation. The estimated far field antenna factor of the sensor system is 83.2dB/m at 28GHz.

An ESL-cancelling circuit for a shunt-connected film capacitor filter using vertically stacked coupled square loops is reported in this paper. The circuit is applicable for a shunt-connected capacitor filter whose equivalent series inductance (ESL) of the shunt-path causes deterioration of filter performance at frequencies above the self-resonant frequency. Two pairs of vertically stacked magnetically coupled square loops are used in the circuit those can equivalently add negative inductance in series to the shunt-path to cancel ESL for improvement of the filter performance. The ESL-cancelling circuit for a 1-μF film capacitor was designed according to the Biot-Savart law and electromagnetic (EM)-analysis, and the prototype was fabricated with an FR4 substrate. The measured result showed 20-dB improvement of the filter performance above the self-resonant frequency as designed, satisfying Sdd21 less than -40dB at 1MHz to 100MHz. This result is almost equivalent to reduce ESL of the shunt-path to less than 1nH at 100MHz and is also difficult to realize using any kind of a single bulky film capacitor without cancelling ESL.

In this paper, a concept of non-contact PIM evaluation method using balanced transmission lines is proposed for impedance-matched PIM measurement systems. In order to evaluate the PIM characteristics of a MSL by using its image model, measurement system using balanced transmission line is introduced. In non-contact PIM measurement, to reduce undesirable PIM generation by metallic contact and the PIM-degradation in repeated measurements, a non-contact connector which is applicable without any design changes in DUT is introduce. The three-dimensional balun composed of U-balun and balanced transmission line is also proposed so that it can be applicable to conventional unbalanced PIM measurement systems. In order to validate the concept of the proposed system, a sample using nickel producing high PIM is introduced. In order to avoid the effect of the non-contact connection part on observed PIM, a sample-configuration that PIM-source exists outside of the non-contact connection part is introduced. It is also shown using a sample using copper that, nickel-sample can be clearly differentiated in PIM characteristics while it is equivalent to low-PIM sample in scattering-parameter characteristics. Finally, by introducing the TRL-calibration and by extracting inherent DUT-characteristics from whole-system characteristics, a method to estimate the PIM characteristics of DUT which cannot be taken directly in measurement is proposed.

With the rapid developments in the Internet of Things (IoT), low power wide area networks (LPWAN) framework, which is a low-power, long-distance communication method, is attracting attention. However, in LPWAN, the access time is limited by Duty Cycle (DC) to avoid mutual interference. Packet-level index modulation (PLIM) is a modulation scheme that uses a combination of the transmission time and frequency channel of a packet as an index, enabling throughput expansion even under DC constraints. The indexes used in PLIM are transmitted according to the mapping. However, when many sensors access the same index, packet collisions occur owing to selecting the same index. Therefore, we propose a mapping design for PLIM using mathematical optimization. The mapping was designed and modeled as a quadratic integer programming problem. The results of the computer simulation evaluations were used to realize the design of PLIM, which achieved excellent sensor information aggregation in terms of environmental monitoring accuracy.

Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.

Synthetic aperture radar (SAR) is a device for observing the ground surface and is one of the important technologies in the field of microwave remote sensing. In SAR observation, a platform equipped with a small-aperture antenna flies in a straight line and continuously radiates pulse waves to the ground during the flight. After that, by synthesizing the series of observation data obtained during the flight, one realize high-resolution ground surface observation. In SAR observation, there are two spatial resolutions defined in the range and azimuth directions and they are limited by the bandwidth of the SAR system. The purpose of this study is to improve the resolution of SAR by sparse reconstruction. In particular, we aim to improve the resolution of SAR without changing the frequency parameters. In this paper, we propose to improve the resolution of SAR using the deconvolution iterative shrinkage-thresholding algorithm (ISTA) and verify the proposed method by carrying out an experimental analysis using an actual SAR dataset. Experimental results show that the proposed method can improve the resolution of SAR with low computational complexity.

The rise of next-generation logistics systems featuring autonomous vehicles and drones has brought to light the severe problem of Global navigation satellite system (GNSS) location data spoofing. While signal-based anti-spoofing techniques have been studied, they can be challenging to apply to current commercial GNSS modules in many cases. In this study, we explore using multiple sensing devices and machine learning techniques such as decision tree classifiers and Long short-term memory (LSTM) networks for detecting GNSS location data spoofing. We acquire sensing data from six trajectories and generate spoofing data based on the Software-defined radio (SDR) behavior for evaluation. We define multiple features using GNSS, beacons, and Inertial measurement unit (IMU) data and develop models to detect spoofing. Our experimental results indicate that LSTM networks using ten-sequential past data exhibit higher performance, with the accuracy F1 scores above 0.92 using appropriate features including beacons and generalization ability for untrained test data. Additionally, our results suggest that distance from beacons is a valuable metric for detecting GNSS spoofing and demonstrate the potential for beacon installation along future drone highways.

Network function virtualization (NFV) technology significantly changes the traditional communication network environments by providing network functions as virtual network functions (VNFs) on commercial off-the-shelf (COTS) servers. Moreover, for using VNFs in a pre-determined sequence to provide each network service, service chaining is essential. A VNF can provide multiple service chains with the corresponding network function, reducing the number of VNFs. However, VNFs might be the source or the target of a cyberattack. If the node where the VNF is installed is attacked, the VNF would also be easily attacked because of its security vulnerabilities. Contrarily, a malicious VNF may attack the node where it is installed, and other VNFs installed on the node may also be attacked. Few studies have been done on the security of VNFs and nodes for service chaining. This study proposes a service chain construction with security-level management. The security-level management concept is introduced to built many service chains. Moreover, the cost optimization problem for service chaining is formulated and the heuristic algorithm is proposed. We demonstrate the effectiveness of the proposed method under certain network topologies using numerical examples.

Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.

This paper presents an analysis-based design method for designing the class-Φ22 wireless power transfer (WPT) system, taking its subsystems as a whole into account. By using the proposed design method, it is possible to derive accurate design values which can make sure the class-E Zero-Voltage-Switching/Zero-Derivative-Switching (ZVS/ZDS) to obtain without applying any tuning processes. Additionally, it is possible to take the effects of the switch on resistance, diode forward voltage drop, and equivalent series resistances (ESRs) of all passive elements on the system operations into account. Furthermore, design curves for a wide range of parameters are developed and organized as basic data for various applications. The validities of the proposed design procedure and derived design curves are confirmed by LTspice simulation and circuit experiment. In the experimental measurements, the class-Φ22 WPT system achieves 78.8% power-transmission efficiency at 6.78MHz operating frequency and 7.96W output power. Additionally, the results obtained from the LTspice simulation and laboratory experiment show quantitative agreements with the analytical predictions, which indicates the accuracy and validity of the proposed analytical method and design curves given in this paper.