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.

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 S_dd21 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.

Non-orthogonal multipe access based multiple-input multiple-output system (MIMO-NOMA) has been widely used in improving user's achievable rate of millimeter wave (mmWave) communication. To meet different requirements of each user in multi-user beams, this paper proposes a power allocation algorithm to satisfy the quality of service (QoS) of head user while maximizing the minimum rate of edge users from the perspective of max-min fairness. Suppose that the user who is closest to the base station (BS) is the head user and the other users are the edge users in each beam in this paper. Then, an optimization problem model of max-min fairness criterion is developed under the constraints of users' minimum rate requirements and the total transmitting power of the BS. The bisection method and Karush-Kuhn-Tucher (KKT) conditions are used to solve this complex non-convex problem, and simulation results show that both the minimum achievable rates of edge users and the average rate of all users are greatly improved significantly compared with the traditional MIMO-NOMA, which only consider max-min fairness of users.

Quantum key distribution or secret key distribution (SKD) has been studied to deliver a secrete key for secure communications, whose security is physically guaranteed. For practical deployment, such systems are desired to be overlaid onto existing wavelength-multiplexing transmission systems, without using a dedicated transmission line. This study analytically investigates the feasibility of the intensity-modulation/direction-detection (IM/DD) SKD scheme being wavelength-multiplexed with conventional wavelength-division-multiplexed (WDM) signals, concerning spontaneous Raman scattering light from conventional optical signals. Simulation results indicate that IM/DD SKD systems are not degraded when they are overlaid onto practically deployed dense WDM transmission systems in the C-band, owing to the feature of the IM/DD SKD scheme, which uses a signal light with an intensity level comparable to conventional optical signals unlike conventional quantum key distribution schemes.

This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.

This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.

Measurements of contact discharge current waveforms from an ESD generator with a test voltage of 4kV are conducted with the IEC specified arrangement of a 2m long return current cable in different three calibration environments that all comply with the IEC calibration standard to identify the occurrence source of damped oscillations (ringing), which has remained unclear since contact discharge testing was first adopted in 1989 IEC publication 801-2. Their frequency spectra are analyzed comparing with the spectrum calculated from the ideal contact discharge current waveform without ringing (IEC specified waveform) offered in IEC 61000-4-2 and the spectra derived from a simplified equivalent circuit based on the IEC standard in combination with the measured input impedances of one-ended grounding return current cable with the same arrangement in the same calibration environment as those for the current measurements. The results show that the measured contact discharge waveforms have ringing around the IEC specified waveform after the falling edge of the peak, causing their spectra from 20MHz to 200MHz, but the spectra from 40MHz to 200MHz significantly differ depending on the calibration environments even for the same cable arrangement, which do not almost affect the spectra from 20MHz to 40MHz and over 200MHz. In the calibration environment under the cable arrangement close to the reference ground, the spectral shapes of the measured contact discharge currents and their frequencies of the multiple peaks and dips roughly correspond to the spectral distributions calculated from the simplified equivalent circuit using the measured cable input impedances. These findings reveal that the root cause of ringing is mainly due to the resonances of the return current cable, and calibration environment under the cable arrangement away from the reference ground tends to mitigate the cable resonances.

This paper proposes an adaptive mixing probability scheme for mixed Gibbs sampling (MGS) or MGS with maximum ratio combining (MRC) in multiple-input multiple-output (MIMO) demodulation. In the conventional MGS algorithm, the mixing probability is fixed. Thus, if a search point is captured by a local minimum, it takes a larger number of samples to escape. In the proposed scheme, the mixing probability is increased when a candidate transmit symbol vector is captured by a local minimum. Using the adaptive mixing probability, the numbers of candidate transmit symbol vectors searched by demodulation algorithms increase. The proposed scheme in MGS as well as MGS with MRC reduces an error floor level as compared with the conventional scheme. Numerical results obtained through computer simulation show that the bit error rates of the MGS as well as the MGS with MRC reduces by about 1/100 when the number of iterations is 100 in a 64×64 MIMO system.

This paper proposes a multibeam digital predistorter (DPD) that suppresses intercarrier interference caused by nonlinear distortions of power amplifiers (PAs) while reducing the power consumption of a multibeam array antenna transmitter. The proposed DPD reduces power consumption by allowing the final PAs of the array antenna transmitter to operate in a highly efficient nonlinear mode and compensating for the nonlinear distortions of the PAs with a unified dedicated DPD per subarray. Additionally, it provides the required high-quality signal transmission for high throughputs, such as realizing a 256-quadrature amplitude modulation (QAM) transmission instead of a 64-QAM transmission. Specifically, it adds an inverse-component signal to cancel the interference from an adjacent carrier of another beam. Consequently, it can suppress the intercarrier interference in the beam direction and improve the error vector magnitude (EVM) during the multibeam transmission, in which the frequency bands of the beams are adjacent. The experimental results obtained for two beams at 28.0 and 28.4GHz demonstrate that, compared with the previous single-beam DPD, the proposed multibeam DPD can improve the EVM. Also, they demonstrate that the proposed DPD can achieve an EVM value of <3%, which completely satisfies the 3GPP requirements for a 256-QAM transmission.

In this paper, we investigate a wireless-powered relays assisted batteryless IoT network based on the non-linear energy harvesting model, where there exists an energy service provider constituted by the hybrid access point (HAP) and an IoT service provider constituted by multiple clusters. The HAP provides energy signals to the batteryless devices for information backscattering and the wireless-powered relays for energy harvesting. The relays are deployed to assist the batteryless devices with the information transmission to the HAP by using the harvested energy. To model the energy interactions between the energy service provider and IoT service provider, we propose a Stackelberg game based framework. We aim to maximize the respective utility values of the two providers. Since the utility maximization problem of the IoT service provider is non-convex, we employ the fractional programming theory and propose a block coordinate descent (BCD) based algorithm with successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to solve it. Numerical simulation results confirm that compared to the benchmark schemes, our proposed scheme can achieve larger utility values for both the energy service provider and IoT service provider.

To localize an unknown wave source in non-line-of-sight environments, a wave source localization scheme using multiple unmanned-aerial-vehicles (UAVs) is proposed. In this scheme, each UAV estimates the direction-of-arrivals (DoAs) of received signals and the wave source is localized from the estimated DoAs by means of maximum likelihood estimation. In this study, by extending the concept of this scheme, we propose a novel wave source localization scheme using a single UAV. In the proposed scheme, the UAV moves on the path comprising multiple measurement points and the wave source is sequentially localized from DoA distributions estimated at these measurement points. At each measurement point, with a moving path planning algorithm, the UAV determines the next measurement point from the estimated DoA distributions and measurement points that the UAV has already visited. We consider two moving path planning algorithms, and validate the proposed scheme through simulation experiments.

Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.