This study explores an advanced method for indoor location estimation leveraging a wireless local area network (WLAN). The integration of round-trip time (RTT), as established by IEEE 802.11mc, has marked a significant advancement in WLANs. This enhancement is pivotal in refining indoor location estimations through multilateration (MLAT) techniques. The RTT, by providing more precise distance measurements between WLAN access points (APs) and users compared to the traditional received signal strength indicator, holds promise for improved accuracy. Nonetheless, in environments with numerous rooms, such as a multi-room floor, the efficacy of MLAT, even with RTT implementation, is not optimal. Addressing this challenge, our paper introduces and validates a novel approach for correcting RTT inaccuracies and a strategic method for selecting APs in MLAT scenarios.

This study discusses an indoor localization method using the radio signal strength indicator (RSSI) of a wireless local area network (LAN). An indoor localization method that adapts a convolutional neural network (CNN) to the fingerprint method is used. In this method, the CNN learns the access point (AP) information for each coordinate using the RSSI and media access control (MAC) addresses obtained from the wireless LAN APs and compares them with the AP information received from the user to estimate the user location. However, data collection for learning is costly when using CNNs. In addition, there is a problem of missing data owing to various factors when collecting AP information. Therefore, data augmentation is proposed as a method to reduce the cost of data collection while maintaining accuracy and is performed after correcting for missing values. However, data augmentation can produce unrealistic data. This paper proposes a method for correcting missing values in measurement data as a solution to this problem.

We confirm the suppression of both fiber-fuse propagation and initiation in standard single mode fibers (SMFs) by using optical pulses with a pulse period of 7.5 μs, for an average input power of 2.0 W and wavelength of 1064 nm. Using the same optical pulses, we demonstrated optical power delivery with a suppressed fiber fuse in a 1.1 km SMF.

The performance of 1-bit Analogue-to-Digital Converter (ADC) receivers is vulnerable to degradation in environments with significant signal amplitude fluctuations, due to quantization errors inherent in binary outputs. Thus, it becomes crucial to verify the effectiveness of 1-bit ADC receivers under such conditions. In this study, we conduct an evaluation of previously proposed 1-bit ADC receivers in Rician and Rayleigh fading channels, where both signal amplitude and phase exhibit dynamic fluctuations. We confirm the effectiveness of 1-bit ADC receivers in such fading environments. However, we observe that the degree of degradation of Signal-to-Noise Ratio (SNR) versus Bit Error Rate (BER) due to signal amplitude and phase variations depends on the employed demodulation method.

Virtual-ESPRIT Algorithm (VESPA), which is a direction-of-arrival (DOA) estimation method using higher-order statistics (fourth-order cumulants), is said to be significant for DOAs estimation of non-Gaussian signals. The authors propose SLS-VESPA, which is VESPA with the SLS (Structured Least Squares) method. Computer simulations show improved characteristics of the SLS-VESPA compared to the conventional TLS (Total Least Squares)-VESPA.

The wireless body area network (WBAN) using higher frequency band than the conventional microwave band WBAN suppresses inter-WBAN interference problem in a user-dense environment. In this paper, we propose a propagation loss model with multi-user blocking and compare with the measurement result of the inter-WBAN interference power at dynamic multi-user environment using wearable directional 2×2 elements patch array antenna for WBAN at 15 GHz. The results show that the distribution characteristics vary with the user density, and antenna directivity coefficient with blockage area scheme improves the accuracy of the propagation model.

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.

As machine learning research in the networking field has become more active in recent years, the demand for network traffic datasets has increased. On the other hand, the amount and types of publicly available network traffic datasets are scarce as training datasets for machine learning. Therefore, we focus on the generative adversarial network (GAN) as a data generation model, aiming to use generated rather than publicly available training datasets. However, existing GANs have difficulty generating sufficiently diverse network traffic to improve generalization ability while representing variations across weekdays, weekends, and date. This study proposes a new layers inserted into the conditional GAN model with the functions of expanding dimensionality of time-series traffic data and embedding temporal position information. Experimental results show that the model with the proposed layers inserted generated diverse network traffic data that represents temporal features.

One possible approach to address communication delay in the complex wireless environment of manufacturing sites is to predict the delay by system-level simulator (SLS) and notify the communication control system in advance. For accurate delay prediction, the carrier sense (CS) model in SLS should incorporate real device characteristics. However, accurate modeling of CS is difficult since its implementation specifications are usually not publicly available. In this letter, we propose a method for estimating CS characteristics using electromagnetic noise generated simulatively by software-defined radio and emitted from a microwave oven and verify the effectiveness of this method.

Aerial base stations (ABSs) have been envisioned as a promising technology toward ubiquitous coverage and seamless high-rate connectivity in sixth-generation (6G) wireless networks. With the inherent mobility but limited communication range, the placement of ABSs should adapt to the time-varying network conditions, e.g., the user distribution and wireless channel state. This letter investigates the cooperative trajectory of UAVs in an integrated terrestrial and non-terrestrial network (TNTN), where unmanned aerial vehicles (UAVs) are deployed as ABSs to supplement the terrestrial macro base station (mBS). We formulate an optimization problem to maximize the number of users with a certain minimum data rate, which is solved using multi-agent reinforcement learning (MARL). Numerical results show that the proposed design is superior to conventional approaches for the cooperative trajectory of UAVs, including K-means clustering-based and single-agent reinforcement learning (SARL)-based methods.

CAPTCHAs have traditionally been used to deter bots, but ensuring protection against machine learning attacks has become difficult. To tackle this problem, we proposed a CAPTCHA based on color constancy, a human cognitive ability that is difficult for machines to reproduce. Although this method achieved high attack resistance while maintaining usability compared to existing CAPTCHAs, the response time increased due to the color selection operation. To address this, we changed the task to a click-type format while keeping the original concept. Our experimental results show that our method matches the response times of existing CAPTCHAs without compromising attack resistance.

This letter proposes a concept of residual noise reduction when using multiple testers for non-contact passive intermodulation (PIM) measurements with near field sensing for array antennas. As described herein, the harmonic balance method and the finite difference time domain method are used for simulation. Examination is conducted for third-order PIMs in two-tone testing in the 2 GHz band. Simulation results indicate that the PIM noise which is weaker than the residual PIM of each tester becomes detectable when using the proposed method. The concept is validated further through experimentation using an equivalent array circuit in the 2 GHz band.

This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.

IEEE802.1Qbv Time-Aware Shaper (TAS) has attracted attention as a technology to realize low-delay communication in various fields. Especially in industrial networks, the demand for low-latency communication between remote areas is increasing, and the realization of a service provider network (SPNW) using TAS is expected. We consider the scheduling scheme of TAS for SPNW. When the scheduling order cannot be controlled, as in SPNW, the bandwidth utilization efficiency is lower than when the scheduling order is ideal. To cope with such problems, we propose a rescheduling scheme focusing on the difference in scheduling difficulty. However, the increase in computational load due to rescheduling has not been discussed. In this paper, we consider a method to reduce the computation time of the rescheduling method.

As a result of the growth of autonomous driving technology, vehicle networks increasingly require not only broadband capabilities and real-time communication, but also enhanced security features. In this paper, we propose a lightweight security switch using hierarchical hash table to achieve security functionality at the central gateway within the vehicle network. Our proposed switch combines header information from Layer-2 to Layer-4 to determine whether incoming packets are attack packets or not. It also achieves fast packet forwarding using low-latency hierarchical hash table. We evaluated the system performance with the FPGA board implementing the proposed security switch, and achieved low-latency transfers of 500 nanoseconds or less.