This study focuses on a rotary LED transmitter (RLT) for image sensor communication (ISC). RLT is a device that improves the transmission rate by utilizing the afterimages generated by moving the LEDs while blinking. When RLT is used in a real environment, the receiver needs to detect the position of the transmitter and the afterimages of lights on the captured image before data demodulation. This paper proposes a method for detecting the position of RLT and the coordinates of afterimages for ISC using RLT. Experimental results show that the proposed method operates correctly without false detection of them in the range of short distances.

We compared the characteristics of overfitting in nonlinear equalizers based on a three-layer artificial neural network (ANN) and a four-layer deep neural network (DNN) for nonlinearity compensation in optical-fiber transmission systems. The characteristics were investigated using training data of a pseudo-random bit sequence (PRBS) and a finite-length repeated random sequence, with a varying number of input- and hidden-layer units in the ANN and DNN. The results showed that the DNN-based nonlinear equalizer had stronger overfitting characteristics for both the PRBS and random sequence, compared to the three-layer ANN-based nonlinear equalizer.

This paper examines a method for fingerprint indoor localization that employs CNN. CNN is trained using AP information. The estimation accuracy of CNN improves as the number of AP information increases. However, gathering AP information is expensive. The problem can be solved using UD (User Data). The UD is unlabeled data because the measuring method does not know the exact location of the user. As a result, we can perform semi-supervised learning with the estimation result as the correct label. In this paper, we propose a method for selecting UD using a CNN-feature extractor.

This study focuses on Wi-Fi fingerprint localization using RNN. Walking survey is a method used for collecting fingerprint datasets. This method can collect continuous walking data while the measurer is walking. However, multiple walking patterns must be measured to accommodate users with different walking speeds and routes. We propose a method for generating simulation data with different walking patterns. The simulation data were created using the coordinate adjacency of the measurement data. Further, we evaluated the model trained with and without the simulation data. Results demonstrate that the accuracy of the proposed method shows its effectiveness.

With 4G networks reaching their practical limits due to the ever-increasing demand for seamless connectivity, it has been clear that 5G is the future of communications. Millimetre-waves (mmWaves) is considered as a way forward with momentous research being carried out to provide cost-effective and easy-to-implement solutions, which can increase the data rate, provide low latency, and reliable radio connection. Inspired by this, a Butler matrix (BM) structure was considered in this work, due to its potential role in implementing large-scale beamforming networks (BFNs) for 5G systems. This would facilitate their deployment in both practical and analytical hybrid beamforming scenarios. Thus, a system-level model of large-scale BMs was realised in software to demonstrate its integrated structures and outputs. The proposed model can be significantly instrumental in the design of large-scale and hybrid wireless infrastructures at both sub-6GHz and mmWave bands.

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.

Orthogonal frequency division multiplexing (OFDM) signals have a high peak-to-average power ratio (PAPR). Although a method using an autoencoder for PAPR reduction has been proposed, it requires a huge amount of computation for both the transmitter and the receiver. In this paper, we propose a method to reduce the computational complexity of the autoencoder. The proposed method is called an asymmetric autoencoder and is an extension of the conventional method. Numerical experiments show that the proposed method can reduce the computational complexity of the receiver.

For the automatic operation of UAV delivery, a route construction method using smart meter radio devices has been studied. We could construct a delivery route that avoids densely populated areas and prevents excessive travel distance based on AODV. However, it is necessary to guarantee arrival time in the delivery service. We propose a route construction method that optimally evaluates traveling time and safety (Proposed Method 1). When multiple UAVs are constructing routes, there are risks of collision between the UAVs due to the use of the same node or links. We propose two methods to construct a route without using the same node and the same link, respectively (Proposed Method 2). The proposed method and the conventional method are evaluated by computer simulation.

A method of constructing a route using “Smart meter” radio devices has been studied for the automatic operation of home delivery by small Unmanned Aerial Vehicle (UAV). Each smart meter node was given information about its own densely populated or scattered residential area, the source node should construct a delivery route to the destination node to avoid the densely populated area. In this study, we adopt OLSR-based route construction because the smart meter network is stable. We propose a method using Dijkstra’s method, which uses the distance between nodes and the density of nodes as costs for selecting the optimal route. The effectiveness and characteristics of the proposed method are evaluated by computer simulation.

This letter investigates a secrecy rate maximum optimization problem of unmanned aerial vehicle (UAV) system with integrated sensing and communication (ISAC) function. To ensure the security of the ISAC-UAV system, we formulate a joint UAV trajectory and transmit power optimization problem to maximize the secrecy rate under the constraints of mobility restrictions, power budgets and sensing requirement. The block coordinate descent (BCD) method is employed to divide the original problem into two sub-problems. Then, the non-convex sub-problems are transformed to convex by the appropriate constraints relaxation and fractional programming method. Eventually, the simulation results verify the effectiveness of our proposed algorithm and demonstrate the trade-off performance between the secrecy and sensing.

In wireless feedback control systems, loss of control packets due to unreliable wireless channels is one of the most important problems. To cope with this problem, we have proposed a novel concept that combines forward error correction (FEC) and selection of important state feedback while keeping a fixed packet length. In this paper, we provide its optimization based on a control layer’s theorem. Numerical results show that the proposed method can achieve the best control performance against communication channel errors.

This paper proposes a polling-scheduling for UWB-based in-vehicle networks. The proposed scheduling aims to reduce the preamble overhead by aggregating periodic data readout of the in-vehicle sensors. Experimental results show that the proposed scheduling successfully suppresses data loss rate compared to the cyclic scheduling.

Broadcasting satellite systems currently use multilevel modulation schemes, which are vulnerable to non-linear distortion, while their on-board amplifiers operate near the saturation point. We have developed a digital pre-distortion (DPD) embedded transmitter and demonstrated its effectiveness over a 12-GHz-band satellite transponder. To improve its versatility, we developed an external DPD adder, which enables existing transmitter to give an analog baseband signal its function. In this study, we confirm the effectiveness of the external DPD adder by evaluating the transmission performance of 64APSK over the satellite transponder.

Secure uplink from smartphone screen to camera communication (SCC) needs to increase the throughput and simultaneously reduce the screen luminance. To realize low-luminance wavelength division multiplexing/space division multiplexing (WDM/SDM) transmission, 2-wavelength adaptive thresholding that categorizes both inter-wavelength and spatial inter-symbol interferences (ISIs) is proposed and experimentally verified. The camera images are categorized into 3×3 cells and the symbol of the center cell is determined. The inter-wavelength ISIs are categorized by the pixel value of the center cell of the other wavelength. The spatial ISIs are categorized by the outer 8-cell symbol pattern estimated at the same wavelength.

In this paper, we present a simple numerical model to describe the propagation characteristics of PL photons in fluorescent optical fibers. Furthermore, we clarify that the photoluminescent intensity distribution is expressed by a simple approximate solution, and we demonstrate its validity through numerical calculations. The experimental results show that the calculated photoluminescent spectra agree well with the measured spectra for fluorescent fiber fabricated by a heat-shrinkable tube, an ultraviolet curable resin, and Lumogen F Red 305 of fluorescent material.

Autonomous distributed power grids have attracted attention as a way to utilize renewable energy to achieve a carbon-neutral society. In order to properly operate these grids, it is necessary to obtain sufficient information on the supply and demand power capabilities and battery health of connected devices in a short time. In addition, methods based on direct current are essential to maximizing the use of renewable energy. This study proposes a method for acquiring information about the energy storage of devices connected to the grid via USB power delivery using deep learning techniques. Furthermore, we propose a method to diagnose the embedded battery health of the device based on short-time monitoring.