A Costas array of size n is an n × n binary matrix such that no two of the $inom{n}{2}$ line segments connecting 1s have the same length and slope. Costas arrays are found by finite-field-based construction methods and their manipulations (systematically constructed) and exhaustive search methods. The arrays found exhaustively, which are of completely unknown origin, are called sporadic. Most studies in Costas arrays have tended to focus on systematically constructed Costas arrays rather than sporadic ones, which reveals the hardness of examining a link between systematically constructed Costas arrays and sporadic ones. This paper introduces a new transformation that preserves the Costas property for some Costas arrays, but not all. We observed that this transformation could transform some systematically constructed Costas arrays to sporadic ones and vice versa. Moreover, we introduce a family of arrays with the property that the auto-correlation of each array and the cross-correlation between any two arrays in this family is bounded above by two.

This paper focuses on a pseudorandom number generator called an NTU sequence for use in cryptography. The generator is defined with an m-sequence and Legendre symbol over an odd characteristic field. Since the previous researches have shown that the generator has maximum complexity; however, its bit distribution property is not balanced. To address this drawback, the author introduces dynamic mapping for the generation process and evaluates the period and some distribution properties in this paper.

Even correlation and odd correlation of sequences are two kinds of measures for their similarities. Both kinds of correlation have important applications in communication and radar. Compared with vast knowledge on sequences with good even correlation, relatively little is known on sequences with preferable odd correlation. In this paper, a generic construction of sequences with low odd correlation is proposed via interleaving technique. Notably, it can generate new sets of binary sequences with optimal odd correlation asymptotically meeting the Sarwate bound.

The calculation of cross-correlation between a sequence with good autocorrelation and its decimated sequence is an interesting problem in the field of sequence design. In this letter, we consider a class of ternary sequences with perfect autocorrelation, proposed by Shedd and Sarwate (IEEE Trans. Inf. Theory, 1979, DOI: 10.1109/TIT.1979.1055998), which is generated based on the cross-correlation between m-sequence and its d-decimation sequence. We calculate the cross-correlation distribution between a certain pair of such ternary perfect sequences and show that the cross-correlation takes three different values.

Low confusion coefficient values can make side-channel attacks harder for vector Boolean functions in Block cipher. In this paper, we give new results of confusion coefficient for f ⊞ g, f ⊡ g, f ⊕ g and fg for different Boolean functions f and g, respectively. And we deduce a relationship on the sum-of-squares of the confusion coefficient between one n-variable function and two (n - 1)-variable decomposition functions. Finally, we find that the confusion coefficient of vector Boolean functions is affine invariant.

A group signature scheme allows us to anonymously sign a message on behalf of a group. One of important issues in the group signatures is user revocation, and thus lots of revocable group signature (RGS) schemes have been proposed so far. One of the applications suitable to the group signature is privacy-enhancing crowdsensing, where the group signature allows mobile sensing users to be anonymously authenticated to hide the location. In the mobile environment, verifier-local revocation (VLR) type of RGS schemes are suitable, since revocation list (RL) is not needed in the user side. However, in the conventional VLR-RGS schemes, the revocation check in the verifier needs O(R) cryptographic operations for the number R of revoked users. On this background, VLR-RGS schemes with efficient revocation check have been recently proposed, where the revocation check is just (bit-string) matching. However, in the existing schemes, signatures are linkable in the same interval or in the same application-independent task with a public index. The linkability is useful in some scenarios, but users want the unlinkability for the stronger anonymity. In this paper, by introducing a property that at most K unlinkable signatures can be issued by a signer during each interval for a fixed integer K, we propose a VLR-RGS scheme with the revocation token matching. In our scheme, even the signatures during the same interval are unlinkable. Furthermore, since used indexes are hidden, the strong anonymity remains. The overheads are the computational costs of the revocation algorithm and the RL size. We show that the overheads are practical in use cases of crowdsensing.

Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

Mobility as a Service (MaaS) is a smart mobility model that integrates mobility services to deliver transportation needs through a single interface, offering users flexible and personalizd mobility. This paper presents a structural approach for developing a MaaS system architecture under Autonomous Transportation Systems (ATS), which is a new transition from the Intelligent Transportation Systems (ITS) with emerging technologies. Five primary components, including system elements, user needs, services, functions, and technologies, are defined to represent the system architecture. Based on the components, we introduce three architecture elements: functional architecture, logical architecture and physical architecture. Furthermore, this paper presents an evaluation process, links the architecture elements during the process and develops a three-layer structure for system performance evaluation. The proposed MaaS system architecture design can help the administration make services planning and implement planned services in an organized way, and support further technical deployment of mobility services.

In this letter, we study the adaptive regulation problem for a chain of integrators in which there are different individual delays in measured feedback states for a controller. These delays are considered to be unknown and time-varying, and they can be arbitrarily fast-varying. We analytically show that a feedback controller with a dynamic gain can adaptively regulate a chain of integrators in the presence of unknown individual state delays. A simulation result is given for illustration.

The robust recursive identification method of ARX models is proposed using the beta divergence. The proposed parameter update law suppresses the effect of outliers using a weight function that is automatically determined by minimizing the beta divergence. A numerical example illustrates the efficacy of the proposed method.

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.

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.

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.