Visible spectrum is an emerging frontier in wireless communications for enhancing connectivity and safety in vehicular environments. The vehicular visible light communication (VVLC) system is a key feature in leveraging existing infrastructures, but it still has several critical challenges. Especially, VVLC channels are highly correlated due to the small gap between light emitting diodes (LEDs) in each headlight, making it difficult to increase data rates by spatial multiplexing. In this paper, we exploit recently synthesized gold nanoparticles (GNPs) to reduce the correlation between LEDs, i.e., the chiroptical properties of GNPs for differential absorption depending on the azimuth angle of incident light are used to mitigate the LED correlation. In addition, we adopt a signal-to-leakage-plus-noise ratio (SLNR)-based precoder to support multiple users. The ratio of RGB light sources in each LED also needs to be optimized to maximize the sum SLNR satisfying a white light constraint for illumination since the GNPs can vary the color of transmitted light by the differential absorption across wavelength. The nonconvex optimization problems for precoders and RGB ratios can be solved by the generalized Rayleigh quotient with the approximated shot noise and successive convex approximation (SCA). The simulation results show that the SLNR-based precoder with the optimized RGB ratios significantly improves the sum rate in a multi-user vehicular environment and the secrecy rate in a wiretapping scenario. The proposed SLNR-based precoding verifies that the decorrelation between LEDs and the RGB ratio optimization are essential to enhance the VVLC performance.

Since office work has become large-scale and diversified in companies or organizations, work engagement and efficiency have been always an important index of a team's or group's evaluation because it is directly connected to their outcomes. In order to identify the group work context, we first need to recognize for what and how long the individual members are spending their time at their desks, but without privacy concerns and underestimation of their actual work. In this paper, we propose and evaluate the base system of personal desk activity recognition by using a low-cost compact WiFi node and its WiFi channel state information (CSI), which can lead to a lightweight group work context identification system. As a result, we achieved 94.2% desk activity recognition accuracy using the on-desk receiver, in recognizing five different classes.

Indoor activity recognition using WiFi sensing is expected to have a wide range of applications, such as monitoring the elderly and home security. The state of radio wave propagation is called Channel State Information (CSI) and can be obtained using specific devices. By collecting CSI and applying machine learning, it is possible to recognize activities. However, CSI is sensitive to changes in the environment, so whenever the arrangement of furniture or the layout of the room changes, it is necessary to re-collect sample data and retrain the model. Retraining a model requires annotation work, which is costly in terms of time and effort. To address this issue, this paper proposes an annotation system that uses backscatter tags to reduce the cost of data collection and model training. In this system, a backscatter tag that generates a frequency shift depending on its angle is attached to a person during data collection, and activity recognition is performed by detecting the presence of the frequency shift. The backscatter tag-based recognition results are then used as pseudo-ground truth for model update.

Reconfigurable intelligent surface (RIS) technology, given its ability to favorably modify wireless communication environments, will play a pivotal role in the evolution of future communication systems. This paper proposes rate maximization techniques for both single-user and multiuser MIMO systems, based on the well-known weighted minimum mean square error (WMMSE) criterion. Using a suitable weight matrix, the WMMSE algorithm tackles an equivalent weighted mean square error (WMSE) minimization problem to achieve the sum-rate maximization. By considering a more practical RIS system model that employs a tensor-based representation enforced by the electromagnetic behavior exhibited by the RIS panel, we detail both the sum-rate maximizing and WMSE minimizing strategies for RIS phase shift optimization by deriving the closed-form gradient of the WMSE and the sum-rate with respect to the RIS phase shift vector. Our simulations reveal that the proposed rate maximization technique, rooted in the WMMSE algorithm, exhibits superior performance when compared to other benchmarks.

Recently, the studies of wireless device-free human sensing technology have dramatically advanced with enabling a variety of applications, from activity recognition to vital sign monitoring. In this paper, we propose Wi-Nod which leverages the Wi-Fi Channel State Information (CSI) to detect head nodding gestures for each Morse code symbol based on time-frequency features for accurate recognition accuracy in multi-human context environment. The system consists of three basic modules: data collection, data preprocessing, and learning part based on the inception model. The model was trained to perform the head movement detection based on the CSI spectrogram collected by the ESP32 nodes. We evaluated the performance of the system on four different data sets collected in two different sessions. Our system achieves over 95% recognition accuracy that reveals the feasibility of Wi-Nod system for real-life deployment.

Time division duplexing (TDD) is adopted to exploit the uplink and downlink channel reciprocity in most of studies on massive multiple-input multiple-output (MIMO) systems. However, even in TDD, a base station (BS) still requires to transmit downlink training signals, which are named in the 3GPP standard as channel state information reference signals (CSI-RSs), to fully support spatial multiplexing in practice. This is because user equipments (UEs) may deploy less number of transmit antennas than receive antennas due to practical issues. Since uplink sounding reference signals (SRSs) are transmitted from only the transmit antennas of the UE, the BS is not able to obtain full downlink MIMO CSI by using channel reciprocity for spatial multiplexing. Hence, after reception of the downlink CSI-RSs, the UE still needs to feed back quantized CSI using a codebook to support spatial multiplexing. Taking practical antenna structures into account for reducing downlink CSI-RS overhead, this paper proposes possible approaches for downlink MIMO CSI reconstruction at the BS using the SRS with quantized downlink CSI to support spatial multiplexing. Numerical results show that the proposed techniques outperform the conventional one, i.e., solely based on the quantized CSI, in terms of the spectral efficiencies of spatial multiplexing.

In the field of crowd estimation, most non-visual approaches confine their objective to only crowd counting, whereas there are a number of vision-based researches which can estimate both the number and location of people. By observation, we figured out that the WiFi channel state information (CSI) also contains the potential characteristics for both estimations. In this paper, we propose a user-device-free simultaneous crowd estimation system that enables both crowd counting and localization simultaneously, by WiFi CSI and Machine Learning. The originality of this study is that we leverage the CSI bundles as the source for extracting features that contain characteristics depending on the dynamic state (counting) and static state (localization). By experiments during three different-time sessions, we confirm that we could achieve up to 94% counting accuracy and 95% localization accuracy by k-fold cross-validation.

To get channel state information (CSI) at a base station (BS), most of researches on massive multiple-input multiple-output (MIMO) systems consider time division duplexing (TDD) to get benefit from the uplink and downlink channel reciprocity. Even in TDD, however, the BS still needs to transmit downlink training signals, which are referred to as channel state information reference signals (CSI-RSs) in the 3GPP standard, to support spatial multiplexing in practice. This is because there are many cases that the number of transmit antennas is less than the number of receive antennas at a user equipment (UE) due to power consumption and circuit complexity issues. Because of this mismatch, uplink sounding reference signals (SRSs) from the UE are not enough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving the downlink CSI-RSs, the UE needs to feedback quantized CSI to the BS using a pre-defined codebook to support spatial multiplexing. In this paper, possible approaches to reconstruct full downlink MIMO CSI at the BS are proposed by exploiting both the SRS and quantized downlink CSI considering practical antenna structures with reduced downlink CSI-RS overhead. Numerical results show that the spectral efficiencies by spatial multiplexing based on the proposed downlink MIMO CSI reconstruction techniques outperform the conventional methods solely based on the quantized CSI.

In recent years, many studies and development of Cyber Physical Systems (CPS) have been carried out to feed back analysis results to human users in a physical space by using machine learning, aiming to analyze a huge amount of information obtained from physical space in a cyber space. To apply CPS to sports, a lot of studies have been conducted on sensing and recognizing actions and movements of athletes using machine learning. In this study, we focus on fishing as a sport, and propose a fishing CPS that recognizes anglers' actions in real-time and provides information on the past useful actions that are linked to fishing results depending on time and place as a decision support when the anglers do not make catch. In addition, this paper reports on the development of an IoT (Internet of Things) device that acquires positional information, acceleration and gyroscope information, and a web system that displays results of real-time activity recognition along with the place and time by animation for realizing the fishing CPS. We have evaluated the developed IoT device and web system from the viewpoint of practical use. As a result, we have confirmed that the GPS and acceleration sensors, in the actual breakwater environment, were constantly transmitting data to a server via UDP communication for 4 hours and 40 minutes.