A visible light communication (VLC) system offers notable advantages, and leveraging image sensors as VLC receivers holds promise. The rolling shutter image sensor stands out as a popular candidate among these sensors. However, challenges arise from signal loss from inter-frame gaps or background scans. This paper addresses these issues by introducing a low-density parity check (LDPC) code and a corresponding code synchronization method suitable for LDPC decoding. Our approach employs a parallel transmitting sequence to achieve synchronization. Notably, the synchronization sequence is transmitted by modulating the luminance of the LED transmitter. Through experimentation, we validate the efficacy of our proposed method in mitigating signal loss.

Channel Bonding is a technique used to aggregate multiple wireless communication channels in order to enhance data transfer speeds. Initially introduced in IEEE 802.11n, it has been adapted to the newer IEEE 802.11ax standard, and so on. While this technology enables faster communications, it also suffers from interference issues due to increased bandwidth utilization. Previous studies have extensively focused on bandwidth selection to minimize channel interference, often requiring significant time. This paper proposes an approach that utilizes the Laser chaos decision maker to rapidly determine the optimal channel for dynamic channel bonding. We design our algorithm based on the scalable laser chaos decision maker and evaluate its performance through computer simulations. The results demonstrate that the proposed method has the capability to make more accurate selections than other algorithms.

We aim to realize a beyond 5th generation (B5G) smart mobile fronthaul (SMFH) that utilizes analog radio over fiber (A-RoF) transmission technology and optical power feed technology by applying newly developed Hollow-Core Fiber. In this paper, to evaluate the feasibility of the SMFH, we will report the successful construction of a A-RoF testbed using an unlicensed WiFi 5 GHz band signal provided by a Linux PC-based access point instead of a licensed 5G sub 6 GHz band signal.

In vehicle platooning, one of the typical applications of Vehicle-to-Vehicle (V2V) communications using C-V2X (Cellular Vehicle-to-Everything), the vehicles need to exchange information such as the velocity and position of a vehicle leading the platoon to achieve safe platoon driving. Relay transmission using sidelink communications, specified by the C-V2X standard, is considered as a potential approach for information exchange in vehicle platooning. The autonomous resource management scheme, SB-SPS (Sensing Based Semi Persistent Scheduling), has been standardized for sidelink communications. However, half duplex problem, one of major problems in the SB-SPS, is not sufficiently resolved for the relay transmission in vehicle platooning. This work proposes an enhanced SB-SPS scheme to mitigate half duplex problem and improve the transmission performance for the relay transmission in vehicle platooning. The performance evaluation through computer simulation demonstrates the effectiveness of the proposed scheme.

With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (DeepJSCC) has attracted attention for efficiently transmitting video and image data. DeepJSCC can maintain a good peak signal-to-noise ratio (PSNR) of images even at a meager signal-to-noise ratio (SNR). In cellular communication systems, the compression ratio must adapt to channel fluctuations, requiring multiple training models at the base station. However, the optimal SNR and compression ratio combination during training has yet to be reported. This paper investigates the necessary number of training models by stepwise varying SNR and compression ratio during training.

CS (Chirp Sequence) radar plays a crucial role in the safety of autonomous driving. However, its widespread adoption increases the probability of wideband inter-radar interference, leading to miss-detection of targets. To address this problem, we utilize RNN (Recurrent Neural Network) and self-attention models to mitigate wideband interference in automotive radar systems and compare 12 different learning models in terms of SNR (Signal-to-Noise Ratio) and processing time.

In recent years, functions in automobiles are required to be processed in real time. This requirement can be realized with SDV (Software-Defined Vehicle), a technology that defines vehicle requirements in software. Although SDV requires automotive SDN (Software-Defined Networking), there has been little discussion on how to implement automotive SDN. This paper studies the feasibility of automotive SDN by realizing four functional requirements proposed as functions required for automotive SDN using P4 and evaluating these requirements through experiments. The feasibility of automotive SDN will be investigated by evaluating these requirements through experiments.

In this letter, I introduce Root Mean Squared Error (RMSE) based on the Fisher information matrix to evaluate the accuracy of distance-based location estimation. This qualitative index, which introduces the concept of the distribution of ranging measurement errors, allows a more precise evaluation of position estimation accuracy than the conventional Position Dilution Of Precision (PDOP). Visualizing PDOP and the proposed RMSE as a distribution over the evaluation space clarifies their respective characteristics. By computer simulations, I show usefulness of the propsoed index by comparing with Least-Squares estimation.

In this study, we developed a light source position estimation system for controlling the wireless-powered drone. An algorithm was designed to estimate the light source position by minimizing an error function consisting of the difference between the measured and estimated values of light-receiving intensity. The received light intensity distribution of a light source with a beam angle of 2.5° was modeled using Gaussian and inverse square functions based on actual measurements. A detection device was fabricated by a photodiode array substrate and a Raspberry Pi. The detection time of the developed system is 97 ms, real-time light source position estimation was confirmed.

This paper introduces a wideband MIMO (multiple input multiple output) antenna operating Sub 6 GHz 5G frequency band with circular polarization (CP). It comprises two identical printed monopole antennas (PMAs) with disconnected ground planes, spaced at 0.051λ₀ (λ₀ is a free-space wavelength at lowest frequency). An inverted U-shaped parasitic element reduces the mutual coupling and improves the axial ratio (AR), while a substrate cavity reflector is installed to be completely CP antenna and increase the absolute gain in the boresight direction. The proposed antenna achieves a measured 10dB impedance and 3dB AR bandwidth of 46.58% (3.03 - 4.95 GHz) with a maximum gain of 5.39 dBic. Notably, polarization in the far-field can be switched between right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) by changing the exciting port. Experimental measurements validate the antenna’s performance within the desirable frequency range.

IoT platforms have been actively discussed for deploying various IoT services using a horizontal approach. The IoT Data Exchange Platform (IoT-DEP) is one of these platforms, standardized as ISO/IEC 30161 series in ISO/IEC JTC 1/SC 41. These standardized documents specify the requirements, architecture, and functional blocks of the communication components. However, detailed communication sequences among the communication components are considered an implementation issue. This paper proposes a content-centric network with Network initiative And Traffic control (C-NAT) applied to the IoT-DEP as a promising candidate for communication sequences. Moreover, it proposes the reinforcement of C-NAT for low-latency and reliable industrial services when applied to IoT-DEP. Specifically, although the original C-NAT is intended to provide cyclic communication, this study proposes support for non-cyclic communication in reinforced C-NAT.

This work explores the impact of optimal power allocation on the physical layer security of lossy decode-and-forward untrusted relay networks using Alamouti Space-Time Block Code (STBC). We introduce the reliable-and-secure probability (RSP) metric to measure the likelihood of successful message recovery at the destination while ensuring the message remains unintelligible to the relays. In high spectral efficiency scenarios, allocating more power to the untrusted relay significantly enhances the RSP, surpassing low spectral efficiency results. A balanced power allocation between the source and the relay, with Alamouti STBC, achieves a high RSP. However, the peak RSP is achieved when the power contributions from the source and the relay are balanced.

The architectural flexibility of the Radio Access Network (RAN) is expected to be enhanced by applying Network Function Virtualization (NFV) to realize low-latency Multi-access Edge Computing (MEC). In order to provide on-demand MEC service tailored to the user’s demands, RAN and the optical network should be constructed quickly without service interruption. In this paper, we propose a network and RAN control method that ensures cooperation of the network controller and RAN controller. It can switch network and RAN at the same time by simple reconfiguration. We evaluate the service interruption time by using the proposed method in some experimental setups in a comparison with the existing method. Finally, we confirm the proposed method enables faster reconfiguration than the network operator method.

Edge computing with offloading over a network is being considered for enabling users to use advanced applications such as autonomous control applications. When network transmission time and/or task-processing time change, the total offloading time also varies. To guarantee low latency of offloading service in this case, over-performance hardware accelerator (HWA) must be assigned on edge computing, which leads to inefficient HWA resource allocation. To address this issue, we propose a method of selecting a HWA corresponding to change of the network transmission time and/or task-processing time. The proposed method refers past transmission times and input data size for the offload, enabling the prediction of changes in network transmission time and/or task-processing time. We evaluated the time for offloading through simulation and found that our method achieves a high percentage of offloads requested by users while efficiently using HWAs.

This article proposes a precoding weight suitable for null-space expansion (NSE) in multi-user massive multiple-input multiple-output (MU-mMIMO). NSE improves inter-user interference (IUI) suppression performance by steering multiple nulls for interfering user terminals (UTs). However, NSE faces challenges in effectively forming beams and nulls for IUI suppression, as well as in managing the computational complexity for precoding. We employ QR decomposition-based Gram-Schmidt orthogonalization (QRGSO) as a precoding algorithm suitable for NSE. Computer simulation demonstrates the superiority of the proposed scheme in terms of IUI suppression performance and computation complexity.

In this paper, we introduce a novel approach called the 2-Step Robust Deep Neural Network (DNN), designed specifically for indoor localization utilizing received signal strength indicator (RSSI) data. This method represents an advancement over the previously proposed 2-Step Extreme Gradient Boosting (XGBoost), aiming to enhance estimation precision by leveraging a single coordinate (x or y) as a feature. The pivotal alterations involve transitioning from XGBoost to DNN and refining the training data to develop a resilient learning model for positional coordinates. Through comprehensive simulations, we demonstrate that the proposed 2-Step Robust DNN attains superior estimation accuracy while preserving the absence of constraints on the dataset.

Intelligent reflecting surfaces (IRSs), which can dynamically control radio propagation, have attracted attention as devices for achieving non-line-of-sight communication under high-frequency bands. Channel estimation is required to control the IRSs, and the overhead of the estimation reduces the transmission efficiency. To remove channel estimation, control methods to predict reflection pattern candidates from image sensing are proposed. However, searching for the appropriate pattern among these candidates is necessary. This paper proposes an IRS control method that recognize users from camera images and selecting reflection patterns, thereby obviating channel estimation and reflection pattern search. Numerical experiments demonstrate that the proposed method realize accurate IRS control by only camera images.