Wi-Fi access points have been widely deployed in homes, offices, and public spaces. Some APs allow users to adjust the antenna orientation to improve communication performance by optimizing antenna polarization. However, it is difficult for non-expert users to determine the optimal orientation, and users often leave the antenna orientation in ineffective positions. To address this issue, we developed a mechanical Wi-Fi antenna device capable of automatically tuning its orientation. Experimental results show that antenna orientation could cause a throughput variation of approximately 70 Mbps under line-of-sight conditions. Furthermore, Bayesian optimization identified better configurations than random search, demonstrating its effectiveness for orientation tuning.

Wireless sensing is an emerging technology with a wide range of applications, but most existing systems capture only the motion of a single point, such as in respiration monitoring. This limitation is critical for tasks requiring multi-point data, such as respiratory volume measurement, where different body points provide distinct information, and a single point cannot represent them all. In this paper, we propose LiSen, a smartphone-integrated LiDAR system for multi-point wireless sensing, and demonstrate its contact-free capability for measuring respiratory volume. LiSen uses smartphone LiDAR to track multiple chest and abdominal points, enabling the first ranging-based spirometer system that captures the full volume curve without new-user calibration. We leverage the unique feature of multi-point sensing to address challenges such as body interference, diverse breathing patterns, and pressure differences. Tests with 35 examinees show that LiSen accurately estimates both instantaneous forced expiratory and inspiratory volume, achieving mean absolute errors below 0.24 L and 0.30 L, respectively, and an 8.93% error for four common pulmonary function indices.

Decentralized federated learning (FL) is a promising approach for training machine learning models on sensor networks, Internet of Things (IoT) devices, and other edge systems where no central server exists. While federated learning offers advantages such as preserving data privacy, it often suffers from non-independent and identically distributed (IID) data distributions across devices, which cause significant performance degradation. This issue is particularly severe when directly optimizing model parameters, because neural network training is inherently non-convex and standard convergence guarantees for convex optimization do not apply. Unlike existing decentralized FL methods that primarily operate in parameter space, we propose federated function-space alternating direction method of multipliers (FedF-ADMM). FedF-ADMM exploits the convexity of loss functionals within function space to derive alternating direction method of multipliers (ADMM)-based update directions, which are subsequently projected onto the parameter space via knowledge distillation. We further introduce a stabilization coefficient to enhance robustness under severe non-IID settings and analyze its behavior from a control-theoretic perspective by interpreting it as a proportional-integral (PI) term. Experiments under challenging non-IID scenarios, including settings where each device has data from only a single label, demonstrate that FedF-ADMM achieves faster and more stable convergence than existing decentralized FL methods, while attaining higher accuracy and better consensus among devices.

There will be an increase in situations where images taken in specific locations are transmitted through networks for various services. However, this trend can lead to significant communication loads due to simultaneous transmission of images from multiple locations. Therefore, it is important to reduce the amount of network traffic in image transmission. While traditional compression methods focus on minimizing information loss in images, some applications only require the retention of semantic information, suggesting potential improvements in communication efficiency. This paper proposes an image generation-based transmission method for highly-efficient communications exploiting composition and descriptive information. The proposed method extracts specific information from an image to decrease the amount of data transmission, and reconstructs the image using an image-generative model by a receiver. In addition, image compression and reconstruction in the proposed method are demonstrated through an experiment. The experimental results indicate a need for a method to evaluate the output and a method for image reconstruction based on this evaluation.

We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.

Child-rearing depression, triggered by the chronic stress of parenting, can lead to serious mental health issues if not detected early. This study uses passive mobile sensing to analyze the behavioral and psychological patterns of households with preschool children. By collecting data from 131 participants (including 18 parents of preschoolers), we aim to differentiate child-rearing anxiety and behavior patterns. Our focus includes step counts, location data, call frequency, and psychological states. Results indicate that parents of preschoolers have fewer steps, visit fewer locations, and have higher call activity. They also show higher stress and anxiety but lower depression levels, suggesting that family support may mitigate depressive symptoms. These insights could aid in developing early detection and intervention strategies for child-rearing depression.

Public awareness of automated external defibrillator (AED) locations is crucial for prompt retrieval in cardiac emergencies. We propose a simulation-based approach as a preliminary step towards developing gamified mobile apps to enhance this awareness. By simulating AED retrieval in real-world pedestrian networks under various scenarios, we identify key elements that can improve retrieval efficiency. Our findings confirm the viability of the framework and highlight crucial aspects for improvement towards efficient future applications.

Recomposing channel state information (CSI) from the beamforming feedback matrix (BFM), which is a compressed version of CSI and can be captured because of its lack of encryption, is an alternative way of implementing firmware-agnostic WiFi sensing. In this study, we propose the use of camera images toward the accuracy enhancement of CSI recomposition from BFM. The key motivation for this vision-aided CSI recomposition is to draw a first-hand insight that the BFM does not fully involve spatial information to recompose CSI and that this could be compensated by camera images. To leverage the camera images, we use multimodal deep learning. We conducted experiments using IEEE 802.11ac devices and revealed that the recomposition accuracy of the proposed multimodal framework is improved compared to the single-modal framework only using images or BFMs.

Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-hop federated distillation (CMFD) proposed in the authors' previous work makes neural network (NN) models get close with others in a function space rather than in a parameter space. Hence, this study solves two unresolved challenges of CMFD: (1) communication cost reduction and (2) visualization of model convergence. Based on a proposed dynamic communication cost reduction method (DCCR), the amount of data transferred in a network is reduced; however, with a slight degradation in the prediction accuracy. In addition, a technique for visualizing the distance between the NN models in a function space is also proposed. The technique applies a dimensionality reduction technique by approximating infinite-dimensional functions as numerical vectors to visualize the trajectory of how the models change by the distributed learning algorithm.

In recent years, vehicle-to-vehicle (V2V) communications are used not only for safe driving assistance such as collision avoidance but also for advanced autonomous driving using dynamic maps and remote control. Ultra-reliable low-latency communications (URLLC) are necessary for safety-related functions, e.g. driver assistance and autonomous driving. Due to high mobility, V2V communication must be performed in a variety of communication environments. However, millimeter wave (mmWave) communications, an enabler of URLLC, are greatly affected by the environment, e.g. the terrain, density of vehicles, etc.. This paper proposes an environment-dependent throughput estimation method for mmWave V2X communications. Roadside units (RSUs) collect information on the throughputs of their nearby vehicles and calculate their distribution model with the Bayesian approach. The proposed method provides a regression scheme to estimate distribution models of the throughput of the area without RSUs. The simulation evaluation validates that the beta distribution fits well for the throughput of mmWave vehicular communication and that the regression scheme can estimate throughput models of the area without RSUs.

In this letter, we investigate the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under Bernoulli–Gaussian model. In addition to the generalized likelihood ratio test (GLRT) proposed in [1], the corresponding Rao test and Wald test are derived in this letter. By observing that obtaining their analytical performance is challenging, we further propose a new probability constraint estimator (PCE) of the unknown sparsity degree. Interestingly, by adopting the PCE, the GLRT, Rao and Wald tests are shown to be statistically equivalent and reduce to a new detector (i.e., the detector with PCE) with a simple structure. The analytical performance of the detector with PCE is thus derived, which is verified by Monte Carlo simulations. Finally, numerical experiments illustrate that the proposed Rao test and the detector with PCE outperform the original GLRT.

With regard to the implementation of WiFi sensing agnostic according to the availability of channel state information (CSI), we investigate the possibility of estimating a CSI matrix based on its compressed version, which is known as beamforming feedback matrix (BFM). Being different from the CSI matrix that is processed and discarded in physical layer components, the BFM can be captured using a medium-access-layer frame-capturing technique because this is exchanged among an access point (AP) and stations (STAs) over the air. This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs. However, the ability of BFM-based sensing has been evaluated in a few tasks, and more general insights into its performance should be provided. To fill this gap, we propose a CSI estimation method based on BFM, approximating the estimation function with a machine learning model. In addition, to improve the estimation accuracy, we leverage the inter-subcarrier dependency using the BFMs at multiple subcarriers in orthogonal frequency division multiplexing transmissions. Our simulation evaluation reveals that the estimated CSI matches the ground-truth amplitude. Moreover, compared to CSI estimation at each individual subcarrier, the effect of the BFMs at multiple subcarriers on the CSI estimation accuracy is validated.

In this study, we extended a contextual bandit algorithm, LinUCB, to facilitate cooperative learning of an optimal strategy with intermittent information sharing. We then applied the algorithm to a Wi-Fi basic service set (BSS) selection problem. The BSS selection problem, in which a mobile user selects a BSS that provides maximal throughput based on information observed, still remains a topic of debate. Reinforcement learning, specifically the multi-armed bandit algorithm, enables mobile users to learn an optimal strategy for selecting a good BSS in their environments. This paper proposes a cooperative contextual bandit algorithm, called Cooperative LinUCB (CoopLinUCB), to address the BSS selection problem. Conventional cooperative bandit algorithms require to share experiences such as context and payoffs for every action, and the information sharing increases communication costs. The proposed algorithm enables mobile users to learn strategies using a limited amount of information that is shared intermittently. The learned strategies are then guaranteed to be equivalent to the strategies that are updated using user experience information. Simulation evaluation based on measured throughput and received signal strength indication fingerprint demonstrates that CoopLinUCB-based BSS selection learns a BSS selection strategy faster and reduces the cumulative regret compared to BSS selection without cooperation.

In millimeter wave (mmWave) vehicle-toinfrastructure (V2I) communications for autonomous vehicles, the small coverage of road side units (RSUs) is an open problem. We propose a multi-hop relaying method using dynamic vehicle deployment in order to increase the coverage of RSUs. The key idea of our method is to leverage the movement controllability of autonomous vehicles to extend the multi- hop relay distance. The proposed deployment method considers blockage because it is a crucial problem in mmWave communications, although it is not a crucial problem in microwave communications. We formulate the deployment problem as an optimization problem and obtain its lower and upper bounds performances. We also introduce a mmWave connectivity graph, from which the vehicle position that achieves the lower bound performance can be obtained by solving a shortest-path problem. Simulation results demonstrate that even when only 7.5% of all vehicles'' positions are controllable, the proposed deployment method can achieve a coverage of 80%, which is more than twice the coverage achieved by the relaying without deployment.

Cooperative perceptions for autonomous vehicles by sharing image sensor data enhance traffic security. For sharing a large amount of sensor data, millimeter-wave (mmWave) communication is expected to be an enabler of high-throughput communications because of its wide band width and its efficiency in spatial reuse. This paper proposes a concurrent scheduling for sensor data dissemination in mmWave vehicular networks at an intersection. The proposed algorithm improves the region covered by shared data in situations where dissemination time is limited and not all data are disseminated. Improvement is realized by prioritizing data to be forwarded considering the geographical information. The priority control enlarges the average of the area of covered region by 15% at maximum. Meanwhile, it is proved that when sufficient time is available for dissemination, the proposed algorithm guarantees that all vehicles can share their data with each other.

In this paper, we propose a receiver using the concept of a "virtual channel" and Turbo codes for multiuser multiple-input multiple-output (MU-MIMO) systems with cooperating transmitters. In order to reduce feedback information from receivers, a precoding method with reduced channel state information (CSI) is needed. Although signals interfere with each other in such a system, the proposed receiver enables high-speed transmission by detecting signals iteratively. In the proposed iterative scheme, soft information is exchanged between virtual channel detectors and Turbo decoders to improve the bit error rate (BER) performance. We evaluate the performance of the proposed receiver in systems with two transmitters, each equipped with four antennas, and two receivers, each equipped with two antennas. The results show that the proposed receiver can improve the system performance owing to the iteration scheme.

In this paper, we propose a virtual layered space-frequency receiver with an ordered successive detector for multiuser multiple-input multiple-output (MU-MIMO) systems where multiple transmitters cooperate with one another. The proposed receiver enables high-speed transmission in the systems even if the transmitters know only partial channel state information (CSI). Actually, the receiver shows such superior performance when the concept of "virtual channel" is applied to cooperative MU-MIMO systems. Moreover, we propose an iterative detection scheme for the receiver; in this scheme, some signals from the Viterbi decoder are fed back to the ordered successive detector. The proposed iterative scheme helps improve the transmission speed when the number of cooperating transmitters is increased. We evaluate the performance of MIMO systems in which the transmitters have four antennas each and two users have two antennas. The receiver can double the transmission speed at the cost of 3 dB in Eb/N0at a BER of 10-4. Furthermore, the receiver can triple the transmission speed at the cost of 7 dB in Eb/N0at a BER of 10-4. When the system has three transmitters, we observe an error floor. However, the proposed iterative method can suppress the error to a value below BER = 10-7.