Mikio Hasegawa

For the Internet of Things (IoT) comprising large-scale networks composed of tiny devices with limited resources, decentralized management is necessary to avoid an increase in load and cost from control signals; in addition, power-saving protocols such as intermittent communications are also essential. Carrier sense multiple access with collision avoidance (CSMA/CA) and time division multiple access (TDMA) have been used as one medium access control protocols in such environments, and CSMA/CA is one of the random access protocols which can be applied to the decentralized system; however, it causes significant delays by stochastic collisions in congested environments. Desynchronization is one of the concepts for decentralized TDMA, and the possibility of a collision can be reduced based on evenly distributed phases by interaction based on the phase differences. There are several desynchronization models, such as the Kuramoto-based model; however, these Kuramoto-based models have not been designed and implemented for IoT devices; it needs to exchange phase information continuously with others within sending cycles, thus, making it difficult to achieve ON/OFF scheduling in one cycle, intermittent communication. To make it applicable to IoT devices, we propose an intermittent inhibitory-coupled Kuramoto oscillator model in this paper. IoT devices can achieve autonomous scheduling to avoid collisions even in high-density environments by the proposed method; therefore, it can guarantee communication quality, moreover, be applied to power-saving IoT devices that use intermittent communication. Simulations and experiments have shown the effectiveness of the proposed method in the environments in both static networks and dynamical networks, supposing mobile environments. Furthermore, compared to CSMA/CA, the proposed method obtains a higher packet success rate than CSMA/CA.

We propose a geometric-difference calculation method (GDCM) to simplify the design and evaluation of a beamforming network using a Luneburg lens between input and output ports for massive multipleinput multiple-output (MIMO) transmissions. This method utilizes positional relationships based on the co-phase plane of a wave passing through the lens. Using numerical analysis, the computational time of the GDCM can be reduced by a factor of 1.5 × 10⁻⁶ or more. In addition, the validity of the GDCM is confirmed through numerical analyses and measurements. The proposed approach enables a cost-efficient beamforming-network design for the rapid development of massive MIMO systems.

In low-power wide-area networks (LPWANs), such as long range (LoRa), the preamble is essential for detecting highly attenuated signals. Its repetitive pattern allows a receiver to identify the presence of the signal and its precise starting point. However, in ultralow signal-to-noise ratio (SNR) environments, the preamble becomes undetectable as it is buried in strong noise, causing the entire detection process to fail. Although existing methods, such as those based on the preamble symbol energy accumulation or deep learning-based spectrogram restoration, have been proposed, their performance remains limited under these extreme conditions. To address this limitation, this article proposes a novel two-stage preamble detection scheme. The first stage employs a convolutional–Transformer encoder–deconvolutional network that leverages self-attention to capture the distinct linear patterns of chirp signals even in the presence of severe noise. In the second stage, a classifier determines the presence of the preamble. Experimental results demonstrate that our proposed method significantly outperforms conventional approaches, lowering the minimum required SNR for preamble detection. To validate its performance, we utilized metrics including true positive rate (TPR) and $F$ -scores. Under these evaluations, our scheme achieves a detection accuracy of over 90% in the ultralow SNR range from −21.7 to −24.3 dB, confirming its robustness and practical viability.

Bluetooth Low Energy (BLE) is widely used in small IoT devices, where further power reduction is required for long-term operation. Previous studies achieved power savings while maintaining communication reliability by adaptively selecting the transmission channel and advertising interval based on a bandit algorithm, treating all combinations as options. This paper investigates the impact of selection structure on performance by comparing this combinatorial method with two alternatives: an independent method selecting the channel and interval separately, and a hierarchical method determining the interval according to the selected channel. Experimental results show that the effectiveness of each selection structure depends on the interference condition, and that the proposed methods reduce power consumption compared with the conventional combinatorial method under the evaluated conditions.