Wi-Fi channel state information (CSI)-based sensing faces challenges in adapting to real-world scenarios due to technical constraints that hinder CSI acquisition. Compressed beamforming report (CBR), standard-compliant channel information in IEEE 802.11ac/ax, is being investigated as an alternative, device-agnostic information source for sensing. In this paper, we implement WiPiCap, a real-time CBR decoding system that is lightweight, easy to deploy, and supports arbitrary antenna, frequency, and bandwidth configurations. In the evaluation, we show that the motion of surrounding objects is reflected in a live plot of CBR data in real time.

To reduce the cost of Internet of Things (IoT) system deployment, we are focusing on the cost reduction of device location information setup and developing a room-by-room device grouping system. In our previous work, we presented a room-by-room IoT device grouping based on wireless LAN (WLAN) channel state information (CSI) using unsupervised learning. The performance in a practical environment, however, is significantly degraded due to the nonuniform time distributions of where people stay in each room. In this paper, we present CSI sampling, namely, a CSI data selection method, relying on independent component analysis (ICA) to improve the device grouping performance in a practical environment. An experimental evaluation conducted in a practical environment reveals that our CSI sampling greatly improved device grouping performance with an adjusted Rand index (ARI) of up to 44.9%.

Applying reconfigurable intelligent surfaces (RIS) to control propagation channels effectively can lead to more efficient 5G networks in terms of both spectrum and energy utilizations. However, the increase in the number of RISs leads to a corresponding rise in power consumption. This paper proposes a novel control method for distributed-RISs by considering a system configuration in which multiple RISs are deployed within the coverage area of a single 5G mmWave BS under dynamic blockage scenarios. The proposed method adaptively selects deployed distributed RISs based on the radio environment to enhance wireless communication quality while simultaneously reducing the power consumption of RISs, thereby contributing to the realization of green 5G mmWave networks. Through ray-trace simulations conducted in the urban environment of Shibuya, Tokyo, we evaluate the effectiveness of the proposed method. The results confirm that it improves received power while achieving approximately a 60% reduction in power consumption.

Wireless sensing research is underway to generate 2D images and 2D videos corresponding to an object or space using the measured amplitude and phase changes during RF signal propagation. The obtained 2D images and 2D videos can be used for object recognition and distance measurement based on image processing techniques. However, 2D images only contain visual information about the sensing target from a specific viewpoint. This paper proposes Channel State Information to Point Cloud (CSI2PC) to enable the observation of a sensing target from multiple viewpoints. CSI2PC generates a 3D point cloud corresponding to the 3D structure of the sensing target from Channel State Information (CSI), which stores the variation of amplitude and phase. CSI2PC generates a 3D point cloud from the measured CSI using a neural network (NN) architecture based on Generative Adversarial Networks (GAN) and Graph Neural Networks (GNN). To ensure that the generated point clouds accurately represent the sensing target, the proposed scheme designs 1) a two-stage learning of the proposed NN architecture and 2) a loss function considering the 3D point cloud reconstruction. Experimental results using consumer Wi-Fi devices show that the proposed CSI2PC can reconstruct a clean point cloud from the measured CSI and accurately classify the object using the point cloud-based classification model.

Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.

Recently, there is growing interest in Wi-Fi CSI-based activity recognition due to its low setup costs. However, accurate CSI-based activity recognition depends on the number of Wi-Fi devices, which is suboptimal cost-wise. Our proposed solution is to use low-power backscatter tags within a Wi-Fi CSI sensing system, collecting multiple CSI data streams from various Wi-Fi channels. This enhances the number of observations without the need to install a large number of Wi-Fi devices. We evaluated classification of five daily activities using traditional Wi-Fi CSI and backscattered CSI, finding an accuracy improvement by combining them.

The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band's small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.

Recent studies have explored various object detection methods that use channel state information (CSI) in wireless local area networks (WLANs) such as IEEE 802.11ac. However, evaluations used with WLAN-based approaches by experimentation mainly target indoor object detection scenarios. Therefore, the effects of antenna placement of access points (AP) and stations (STA) on detection performance in outdoor environments have not been clarified. As described herein, by experimentation, we evaluate the performance of our developed WLAN-based object detection system with distributed antennas in outdoor and large-scale indoor scenarios. Then we clarify the effects of AP and STA placements on the achieved detection performance, where an off-the-shelf WLAN device is used to capture feedback CSI, including beamforming weight information. We leverage these data as feature information for machine learning (ML)-based object detection. Findings indicate that the object detection method can increase the detection probability effectively in specific locations by appropriate location of AP and STA, while reducing the necessary complexity for ML model training and detecting the target in outdoor and large-scale indoor scenarios.

Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been investigated intensively. Further improvements of detection performance will depend on selecting appropriate feature information and determining the placements of distributed antenna elements. This paper presents a proposal of an enhanced device-free WLAN-based localization scheme with beam-tracing-based antenna placement optimization and spatially localized regression, where beam-forming weights (BFWs) are used as feature information for training machine-learning (ML)-based models localized to partitioned areas. By this scheme, the antenna placement at the access point (AP) is determined by solving a combinational optimization problem with beam-tracing between AP and station (STA) without knowledge of the CSI. Additionally, we propose the use of localized regression to improve localization accuracy with low complexity, where classification and regression-based ML models are used for coarse and precise estimations of the target position. We evaluate the proposed scheme effects on localization performance in an indoor environment. Experiment results demonstrate that the proposed antenna placement and localized regression scheme improve the localization accuracy while reducing the necessary complexity for both off-line training and on-line localization relative to other reference schemes.

The evolution of a cell phone network and a wireless local area network (LAN) has enabled various devices to be connected to wireless networks anytime, anywhere. This has led to the emergence of various applications, such as automatic transportation of parts and cargo by automated guided vehicles, remote control of unmanned vehicles, and drones. It will be a challenge to maintain high-quality connections, which satisfy the requirements of such emerging applications, for a large number of intelligent connected devices. To improve the network performance, we must control the wireless network setting and the behavior of the connected vehicles, i.e., robots. From this perspective, this proposes a novel framework, CoRein, that achieves simultaneous optimization of robot behavior and wireless network setting. In contrast to the existing studies, CoRein provides optimal actions for robots and wireless networks using deep reinforcement learning (DRL) architecture. To the best of our knowledge, our study firstly attempts to solve different tasks corresponding to robots and networking with a unique DRL architecture. To evaluate the effectiveness of CoRein, we considered a scenario where two robots were moving back and forth while communicating with one of two access points. CoRein calculates the robot velocities and AP selection. We carried out simulations and indoor experiments with our robot testbeds to evaluate the network performances. Evaluation results confirmed that the two robots adjusted their positions and the access points to increase the wireless network's performance, i.e., throughput and round trip time while changing the speed of their movements.

Millimeter wave (mmWave) is an indispensable technology in 5G communication system, as its wider bandwidth assures larger channel capacity than previous 4G/LTE communication system. However, due to the high path loss in mmWave band, the deployment of more base stations is required to achieve the same coverage. Massive analog relay MIMO system, in which massive analog relay stations are deployed to construct artificial channel and enhance communication coverage, had been proposed to solve this issue. In this paper, we further consider multi-user MIMO scenario using massive analog relay MIMO system. We introduce a beam selection algorithm and resource block allocation algorithm to utilize the resource and improve the performance of the whole multi-user MIMO system. In addition, the feasibility and validity of this method is demonstrated by numerical simulation showing that the channel capacity can be improved.

Various object detection schemes using channel state information (CSI) in wireless local area networks (WLANs) such as IEEE802.11ac have been investigated recently. For further detection performance improvement, adopting a proper feature selection technique and using an appropriate antenna placement to obtain more effective CSI is important As proposed herein, a device-free WLAN-based object detection scheme with reflectors is developed, where the feedback CSI in WLANs (i.e., beamforming weight information) is captured at an off-the-shelf WLAN device and is used as feature information for machine learning (ML)-based object detection. In this scheme, reflectors are placed to create a multipath rich condition in an observation area. By experimentation, we demonstrate that reflectors can improve object detection performance. Additionally, we clarify that proper antenna placement effectively improves the detection accuracy, even when using a small number of antenna elements in an indoor environment. Experiment results demonstrate that the object detection performance of WLAN systems in an indoor environment can be improved by selecting antenna placement appropriately and by using measured concatenated CSI as effective feature information.

Wireless local area network (WLAN) based object detection techniques have attracted much attention. However, the detection performance of WLAN-based object detection is highly dependent on the antenna topology used. In this paper, we experimentally evaluate performance of antenna topology on the achieved object detection performance using channel state information (CSI) in WLAN systems, where spatially concatenated CSI is utilized as effective feature information for machine learning (ML)-based object detection. Based on the experiment results, we discuss how much object detection performance improvement is expected when the proper antenna topology is used at the access point side. We also experimentally clarify that the detection accuracy is improved when concatenated CSI is used as feature information for ML-based object detection for various antenna placement scenarios.

An intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflective elements. The IRS is expected to both solve the problems of high-frequency band communication, such as vulnerability to obstacles and large distance attenuation, and realize ultra-multiple connections in the high-frequency band. However, the reflective elements of the IRS can only be controlled by time division, which means that the IRS can support only one user per time slot, whereas frequency resource division communication can be used by base stations to support multiple users. Therefore, in this study, we investigated a method that enables an IRS to support the communication of multiple users simultaneously. The proposed method determines the frequency allocated to each user and expands the reflected beamwidth of the IRS to maximize the effect of the frequency shift. This approach takes advantage of the misalignment in the reflection direction for each frequency that occurs when multiple frequencies with the same phase shift are incident on the IRS. Simulation experiments show the effect of allocation considering the misalignment in the reflection directions and the effect of expanding the beamwidth in a certain environment at 28 GHz. The results show the potential of this approach in IRS-based multi-user communication systems.

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.

An intelligent reflecting surface (IRS), which comprises numerous passive elements, is considered a promising technology for smart wireless communication. However, the passive characteristics of an IRS render the explicit estimation of the channel state information to appropriately adjust its reflection coefficient challenging. This study proposes a deep reinforcement learning-based algorithm that learns the precoding vector of the base station (BS) and the IRS phase shift from the wireless environment to address this problem. We develop a beam-pattern-based learning framework for this algorithm that indirectly maps the wireless environment to the phase shift to manage the large state-action space caused by the multiple elements of the BS and IRS. Based on the simulation results, the proposed algorithm can learn from the environment and establish a transmission strategy that improves the user's transmission rate. Furthermore, the results validate that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that directly builds a mapping to the phase shift.

In this study, we propose a Put-and-Play (PnP) Internet of Things (IoT) system, an IoT system that requires no initial setup. IoT systems require the initial setup consisting of device location information setup, network configurations, and device coordination. Although configuration automation and assistant methods for network configurations and device coor-dination have been proposed, device location information setup still needs manual operations. This paper therefore proposes a room-by-room device grouping method that groups IoT devices in the same room. We utilize IEEE 802.11ac Channel State Information (CSI) to group IoT devices in the same room with a non-supervised learning algorithm. Experimental evaluations conducted in a smart house environment reveal that our device grouping method successfully groups IoT devices in the same room with an adjusted Rand index (ARI) of up to 1.00.

In recent years, intelligent reflecting surfaces (IRSs) for large-capacity and highly reliable wireless communication have attracted widespread attention. However, a multiuser access system with multiple IRSs poses limitations in reducing the large signaling overhead of channel estimation for numerous links between the IRSs and users. One approach to reduce the exhaustive channel estimation involves associating the IRS with a user and performing beam tracking for a certain period. However, as the IRS–user association is fixed during the tracking period, the dynamic variations in their link status caused by user mobility degrade the system performance if the association is decided without prior planning. Therefore, this paper proposes an IRS–user association strategy considering user mobility for IRS-aided multibeam transmission systems. Contrary to prior works, our association strategy aims to optimize the long-term performance of systems in terms of capacity and reliability. The proposed strategy minimized performance degradation even under drastic fluctuations of link conditions, thereby reducing channel estimation overhead because both the IRS and user can be associated for long periods with low performance degradation.

Machine learning based object detection that utilizes channel state information (CSI) in wireless local area network (WLAN) systems is an effective approach for indoor positioning. In this paper, we propose a real-time CSI-based object detection scheme using interleaved subcarrier selection techniques for WLAN systems with distributed antennas, where CSI frames are collected and used as data-set for machine learning and object detection. To improve real-time detection performance, we investigate two approaches; interleaved sampling (IS), and interleaved sampling and clustering (ISC). In the IS scheme, a part of subcarriers are selected among all subcarriers in an interleaved manner to reduce data-set size while maintaining the object detection accuracy. In the ISC scheme, all subcarriers (their CSI) are grouped into several clusters in an interleaved manner and detect a target by integrating cluster-by-cluster machine-learning results. Furthermore, we demonstrate the effectiveness of the proposed approach through real-time experimental evaluations in an indoor environment scenario. Experimental results show that the ISC scheme improves object detection probability than the case without clustering, while the IS scheme is effective in reducing data-set size for obtaining almost the same performance. The results also indicate that the improved detection performance is obtained by using the proposed scheme with a distributed antenna array.

There is a demand for device-free user location estimation with high accuracy in order to realize various indoor applications. This paper proposes an IEEE 802.11ac explicit feedback-based channel state information (CSI) learning system which can be used for device-free user location estimation. The proposed CSI learning system captures CSI feedback from off-the-shelf Wi-Fi devices and extracts 624 features from a CSI feedback frame defined in IEEE 802.11ac. We evaluated the proposed system using location estimation with six patterns: different combinations of device-free user movement and access point antenna orientation. The evaluation results show that the machine learning based localization achieves approximately 96% accuracy for seven positions of the user, and the divergence of CSI improves localization performance.

Multiple-input multiple-output (MIMO) systems with implicit feedback utilize uplink channel state information (CSI) for downlink transmit beamforming, relying on over-the-air channel reciprocity. We develop real-time hardware for multiuser MIMO transmission with implicit feedback based on orthogonal frequency division multiplexing (OFDM), with field programmable gate arrays (FPGAs). This paper presents indoor experimental results by the real-time hardware, employing several calibration schemes for uplink CSI. In particular, we investigate performance of our proposed scheme, weighted-combining calibration (WCC), in which multiple calibration coefficients are calculated and then combined with weights based on channel gain, to improve the calibration accuracy. It is shown that WCC improves real-time throughput considerably, compared with equal gain-combining calibration (EGCC), a scheme which combines coefficients with equal weighting.

This paper proposes a novel calibration scheme for implicit feedback beamforming on downlink (DL) multiuser (MU) multiple-input multiple-output (MIMO) systems. In the proposed scheme, an access point (AP) calculates calibration coefficients from ratios of DL and uplink (UL) channel state information (CSI) corresponding to multiple stations (STAs), and then combines multiple coefficients with minimum mean square error (MMSE) weights. Although it is difficult to calculate MMSE weights since Gaussian noise terms due to estimated CSI error exist in the denominator of the mean square error, we derive the weights using a linear approximation in the highsignal to noise power ratio (SNR) regime. Simulation results reveal that the proposed scheme obtains signal to interference plus noise power ratio (SINR) gain of more than 6.9 dB over a calibration scheme without combining, at 10% cumulative distribution function (CDF) and SNR of 40 dB. It is found that the proposed scheme improves the calibration accuracy considerably.

Multiuser MIMO (MU-MIMO) transmission offers higher channel capacity than SISO transmission in practical system. However, MU-MIMO channel capacity in multi-cell environment is limited by inter-cell interference (ICI). Distributed zero forcing beamforming (DZFBF) is one of candidates to improve MU-MIMO channel capacity by mitigating ICI. To evaluate the performance and feasibility of DZFBF, we have developed a real-time testbed implemented on a FPGA. For this, we also implement a simple weight generation process that uses feedback channel state information from STAs; it is a extension of the weight generation from MU-MIMO systems. Moreover, we demonstrate the real-time transmission performance of our testbed in an actual indoor multi-cell environment. The experimental results indicate that the effectiveness of DZFBF is twice as high as that of MU-MIMO transmission with TDMA.

Multiuser (MU) - multiple input multiple output (MIMO) techniques are attractive for increasing the downlink spectrum efficiency while requiring few antennas at each user station (STA). As practical precoding methods for multi-user MIMO systems, block diagonalization (BD) and successive optimization (SO) algorithms have been investigated using the metric of channel capacity. However, the throughput of SO algorithms has not been evaluated for wireless LAN systems. This paper focuses on the two-STA scenario where a close STA lies in the very near field of the access point (AP) while the distant STA lies farther from the AP; it proposes MU-MIMO transmission based on an SO algorithm that uses only the channel state information (CSI) of the close STA. The proposed method determines the transmission weight for the distant STA so as to nullify the close STA and reduces the inter-user interference at the distant STA by decreasing the transmission power for the close STA. Thus, the distant STA does not have to implement the CSI feedback function. Simulations compare zero forcing (ZF), BD, and proposed SO algorithms and confirm the conditions wherein the proposed method outperforms single user MIMO and multiuser MIMO based on ZF and BD algorithms.

Multiuser (MU) - multiple input multiple output (MIMO) techniques are attractive to increase the downlink spectrum efficiency since they can accept a small number of antennas at each user station (STA). In MU-MIMO systems, the transmission performance is affected by the STA combination that is spatially multiplexed in the same frequency and same time slot. Thus, the AP needs to determine the optimum STA subset and the number of spatially multiplexed STAs from the channel state information. In this paper, we propose a user selection scheme that uses an iterative algorithm to determine the number and the optimal combination of multiplexed STAs. The proposed scheme chooses STAs one by one so as to improve the throughput of all STAs in the subset compared to that achieved in single-user MIMO transmission. Thus, all STAs have a fair chance of being selected even when their SNRs vary widely. Furthermore, the iterative algorithm has lower calculation complexity than the exhaustive search approach. The index employed for user selection is also simplified by using the signal-space vectors of the channel matrices. Computer simulations confirm that the proposed method improves the increase in STA throughput over single-user MIMO throughput in a wide-range of SNR values while equalizing STA selection probabilities.

Multiple-Input Multiple-Output (MIMO) system has been actively investigated to enhance wireless data transmission in outdoor environments. Micro/Macro-cell services such as WLAN/WiMAX have been much attention because they achieve a high throughput. Since proliferation of these services are expected, base stations (BSs) need to be located at various heights and locations in outdoor environments. Although many publications have reported measurements of the channel state information (CSI) in outdoor environments, the relationship between the BS heights or locations and the transmission performance in MIMO systems has not yet been fully investigated. Moreover, when the number of BSs increases per area, inter-cell interference problem occurs. Such an interference deteriorates the communication quality. In this paper, the experiment is conducted in urban area, in Japan, and the CSI was obtained using 4 × 4 MIMO-OFDM signals. We present signal to noise ratio (SNR), channel capacity and eigenvalues in MIMO channels, when the BS heights and locations are changed. We clarify that eigenvalues are one of important parameters for not only the channel capacity in MIMO channels but also inter-cell interference by the analysis using the measured MIMO channels.