Kazuto Yano

The demand for automation in Industry 4.0 is driving the growth of wireless Industrial Internet of Things (IIoT) devices. In smart manufacturing, maintaining high reliability and high throughput of Wireless Local Area Networks (WLANs) are equally important for ensuring production safety and efficiency. Terahertz (THz) communication offers high-data-rate and low-latency transmission but faces challenges from coverage and blockage issues. Multi-Access Point (AP) joint transmission allows multiple APs to serve same users simultaneously, and Intelligent Reflecting Surface (IRS) enables the beyond Line-of-Sight (LoS) transmission to bypass obstacles. The integration of these technologies greatly enhances signal availability and system capacity. However, the load balancing problem still remains a critical issue in IIoT networks. Uneven traffic distribution can cause some AP to become overloaded while others remain underutilized. In dynamic industrial scenarios, such imbalance limits the network ability to respond to changing traffic demands. In this paper, we investigate the load balancing problem in IRS-aided THz joint transmission for IIoT networks. A cooperative game-theoretic method is proposed to maintain load balance and user demand satisfaction. Simulation results show that our method improves both network reliability and overall performance.

This study proposes a link availability prediction framework for sub-terahertz wireless local area networks (LANs). In the sub-terahertz band, to combat severe path loss between an access point (AP) and a station (STA), it is necessary to form a narrow beam. Exhaustive beam search due to a large number of candidate beams causes significant processing overhead in establishing wireless channels between an AP and a STA. To address this issue, we propose a novel framework to predict link availability that utilizes the propagation information of lower frequency bands. To incorporate realistic channel characteristics, we measured the propagation characteristics of signals in the millimeter wave and microwave bands using off-the-shelf wireless devices under various AP/STA positions and orientations. We analyzed the measured characteristics of each band by quantifying the fluctuation and separability of the features in both linear and nonlinear spaces. The analysis revealed complementary characteristics: channel state information (CSI) in the microwave band showed lower fluctuation and higher separability in linear space, while the received signal strength indicator (RSSI) in the millimeter wave band showed greater robustness in linear space and better separability in nonlinear space. Motivated by this complementarity, we designed a probabilistic neural network (PNN)-based architecture that integrates neural encoders to predict the link availability of the sub-terahertz band. Numerical experiments using the measured data demonstrate high performance, and show robustness to fluctuations and adaptability to variations in AP/STA positions and orientations. This study verifies the effectiveness of the proposed link prediction framework for sub-terahertz wireless LANs.

Recent advances in quantum computing have prompted urgent consideration of the migration of classical cryptographic systems to post-quantum alternatives. However, it is impossible to fully understand the impact that migrating to current Post-Quantum Cryptography (PQC) algorithms will have on various applications without the actual implementation of quantum-resistant cryptography. On the other hand, PQC algorithms come with complexity and long processing times, which may impact the quality of service (QoS) of many applications. Therefore, PQC-based protocols with practical implementations across various applications are essential. This paper introduces a new framework for PQC standalone and PQC–AES (Advanced Encryption Standard) hybrid public-key encryption (PKE) protocols. Building on prior results, we focus on securing applications such as file transfer, video streaming, and chat-based communication using enhanced PQC-based protocols. The extended PQC-based protocols use a sequence number-based mechanism to effectively counter replay and man-in-the-middle attacks and mitigate standard cybersecurity attack vectors. Experimental evaluations examined encryption/decryption speeds, throughput, and processing overhead for the standalone PQC and the PQC–AES hybrid schemes, benchmarking them against traditional AES-256 in an existing client–server environment. The results demonstrate that the new approaches achieve a significant balance between security and system performance compared to conventional deployments. Furthermore, a comprehensive security analysis confirms the robustness and effectiveness of the proposed PQC-based protocols across diverse attack scenarios. Notably, the PQC–AES hybrid protocol demonstrates greater efficiency for applications handling larger data volumes (e.g., 10–100 KB) with reduced latency, underscoring the practical necessity of carefully balancing security and operational efficiency in the post-quantum migration process.

Recent advances in quantum computing have prompted urgent consideration for the migration of classical cryptographic systems to post-quantum alternatives. However, it is impossible to fully understand the impact that migrating to current Post-Quantum Cryptography (PQC) algorithms will have on various applications without actual implementation of quantum-resistant cryptography. On the other hand, PQC algorithms come with complexity and long processing times, which may impact the Quality of Service (QoS) of many applications. Therefore, PQC-based protocols with practical implementation across various applications are essential. This paper introduces a new framework for PQC standalone and PQC-AES (Advanced Encryption Standard) hybrid Public Key Encryption (PKE) protocols. Building on prior results, we focus on securing applications such as file transfer, video streaming, and chat-based communication using enhanced PQC-based protocols. The extended PQC-based protocols use a sequence number-based mechanism to effectively counter replay and man-in-the-middle attacks and mitigate standard cybersecurity attack vectors. Experimental evaluations examined encryption/decryption speeds, throughput, and processing overhead for these standalone PQC and PQC-AES hybrid schemes, benchmarking them against traditional AES-256 in an existing client-server environment. The results demonstrate that the new approaches achieve a significant balance between security and system performance compared to conventional deployments. Furthermore, the comprehensive security analysis confirms the robustness and effectiveness of the proposed PQC-based protocols across diverse attack scenarios. Notably, the PQC-AES hybrid protocol demonstrates greater efficiency for applications handling larger data volumes (e.g., 10–100 KB) with reduced latency, underscoring the practical necessity of carefully balancing security and operational efficiency in the post-quantum migration process.

Demands for high-speed wireless connectivity are pushing the boundaries of Wireless Local Area Networks (WLANs), prompting a transition towards Terahertz (THz) technology for 6G networks. THz technology is capable of supporting higher data rates and lower latency, enabling the miniaturization and denser placements of Access Points (APs), particularly in urban environments. Joint transmission strategies, coordinating data from multiple APs, can further improve THz network performance and reliability. Despite its advantages, THz signals face challenges with signal penetration and attenuation. To tackle these issues, Intelligent Reflecting Surfaces (IRS), a planar surface made with reconfigurable meta-material elements, have emerged as a solution with the abilities to create beyond Line-of-Sight (LoS) communication and passive beamforming. In this paper, we study the channel estimation and resource allocation problem with considering the user mobility and increased system complexity. A combined clustering and Kalman Filtering (KF) approach is proposed to continuously estimate user statuses and efficiently organize network elements. The simulation results demonstrate that our approach markedly enhances the performance of joint transmission and reduces the overhead associated with channel estimation, thereby highlighting the potential of integrating THz joint transmission and IRS in future WLANs.

For a millimeter wave (mmWave) intelligent re-configurable surface (IRS)-MIMO system, if it can correctly detect the interference occurrence and their locations, the patterns of interference signal can be collected and learned using machine learning for the prediction of interference arrival. With the information of interference location and activity pattern, the capacity of the system can be largely improved using many techniques such as beamforming, interference cancellation, and transmission scheduling. This paper aims to detect interference occurrence using a low-complexity MUSIC (MUSIC: multiple signal classification) spectrum-based method, and then localize their sources for mmWave IRS-MIMO system. The MUSIC spectrum of wireless system can be regarded as somehow the ‘signature’ related to the signals transmitted from different users or interference. We utilize such property to detect the occurrence of interference, and then localize their sources in a low-complexity way. Finally, the pattern of interference occurrence can be learned to predict the interference arrival from the collected data. This paper also proposed an efficient probabilistic neural network (PNN)-based predictor for the interference arrival prediction and showed its prediction accuracy. From simulated results, our proposed method can achieve the correct results with the accuracy near to 100% when the fingerprint samples is over 10. In addition, the localization error can be within 1 m with more than 65% and 43% for Y-axis and X-axis, respectively. Finally, based on the results of the interference occurrence, the proposed PNN-based predictor for the interference arrival prediction can capture correctly the similar distribution function of the coming continuous idle status.

Intelligent reflecting surface (IRS), which can reflect radio waves controlling the phase of incident radio waves, is being investigated for wireless communication in high-frequency bands. To control the reflection characteristic, it is necessary to separately estimate a large number of channel coefficients between transmitting and receiving antennas through each IRS element. This causes significant overhead for the channel estimation. We have proposed a channel prediction method to reduce the overhead using Gaussian process regression with spectral mixture kernel. In Gaussian process regression, the determination of the hyperparameters used to calculate the kernel matrix has a significant impact on prediction accuracy. In this study, we propose validation-based hyperparameter determination for GPR-based channel prediction and evaluate the performance difference between the gradient method and validation.

The rapid growth in the IoT network comes with a huge security threat. Network scanning is considered necessary to identify vulnerable IoT devices connected to IP networks. However, most existing network scanning tools or system do not consider the burden of scan packet traffic on the network, especially in the IoT network where resources are limited. It is necessary to know the status of the communication environment and the reason why network scanning failed. Therefore, this paper proposes a multimodel-based approach which can be utilized to estimate the cause of failure/delay of network scanning over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, the factors that cause network scanning failure/delay were identified and categorized. Then, using a machine learning algorithm, we introduced a multimodel linear discriminant analysis (MM-LDA) to estimate the cause of scan failure/delay based on the results of network scanning. In addition, a one-to-many model and a training data filtering technique were adopted to ensure that the estimation error was drastically reduced. The goal of our proposed method was to correctly estimate the causes of scan failure/delay in IP-connected devices. The performance of the proposed method was evaluated using computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. The proposed MM-LDA correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various scenarios. In comparison to other conventional machine learning classifiers, the proposed MM-LDA outperforms various classification methods in the estimation of the cause of scan failure/delay.

Access point (AP) coordinated spatial reuse with Q-learning enables efficient spectrum utilization [1]. Although sharing of transmission schedules among APs is necessary for coordination, there is no mechanism to identify the APs with which the schedules are to be shared, resulting in excess information being shared among APs. In this study, we propose a scheme to identify the interference-free APs that are not required for sharing of information by comparing Q-values. A simple simulation demonstrates that this scheme successfully reduces shared information without throughput degradation.