Koji Yamamoto

Kyoto Institute of Technology

In this study, we analyzed the beamforming feedback (BFF) as a WiFi sensing feature. The BFF is used for eigenmode transmission and is calculated from the channel state information (CSI), which includes the fine-grained information mirroring propagation characteristics. Owing to its accessibility, BFF has attracted attention as an alternative WiFi...

This paper proposes a novel device-free sensing framework, integrative heterogeneous wave sensing (IHWS). IHWS leverages mmWave angular power profile obtained through beam search and acoustic waves to facilitate wireless sensing focused primarily on blockage prediction in the realm of 6G communication networks. For reliable mmWave communication in...

Federated learning (FL) is an emerging machine learning (ML) framework that enables the use of often privacy-sensitive smart city data (e.g., pictures from the road surveillance cameras and connected vehicles) for machine learning. However, in the smart city scenario, data-holder devices called clients are heterogeneous, and their computation and c...

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine le...

In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robu...

Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the br...

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 pro...

Recompositing 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 recompos...

This letter proposes a practical transmission datarate adaptation (TDA) scheme using Q-learning applicable to IEEE 802.11ax wireless local area networks. In the proposed scheme, each basic service set (BSS) selects an appropriate transmission datarate according to the buffer statuses of adjacent BSSs which are periodically collected and the transmi...

The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a c...

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM) that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the...

As a step towards establishing reliable broadcast wireless local area networks (WLANs), this paper proposes acknowledgement (ACK)-less rate adaptation to alleviate reception failures at broadcast recipient stations (STAs) using distributional reinforcement learning (RL). The key point of this study is that the algorithms for learning the strategy o...

In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm that does not require a pre-obtained database. This is...

This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces . Considering that the representati...

The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a c...

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM)that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the s...

With the growing demand for wireless local area network (WLAN) applications that require low latency, orthogonal frequency-division multiple access (OFDMA) has been adopted for uplink and downlink transmissions in the IEEE 802.11ax standard to improve the spectrum efficiency and reduce latency. In IEEE 802.11ax WLANs, OFDMA resource allocation that...

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 l...

This paper proves that the angle of departure (AoD) estimation using the multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax, is possible. Although channel state information (CSI) enables model-driven AoD estimation, most BFF-based sensing techniques are data-driven becaus...

Millimeter wave (mmWave) beam-tracking based on machine learning enables the development of accurate tracking policies while obviating the need to periodically solve beam-optimization problems. However, its applicability is still arguable when training-test gaps exist in terms of environmental parameters that affect the node dynamics. From this ske...

This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs) contained in the captured frames, which is a rotation matrix of channel state information (CSI). BFMs are transmitted unencrypted and easily obtained using frame capturin...

This paper demonstrates the feasibility of received power strength indicator (RSSI)-based single-antenna localization (R-SAL) with decimeter-level localization accuracy. To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information) or coarse-grained RF information (e.g., RSSI) from more...

The distributed inference framework is an emerging technology for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In distributed inference, computational tasks are offloaded from the IoT device to other devices or the edge server via lossy IoT networks. However, n...

In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs), data rate control that is based on acknowledgement (ACK) mechanism similar to the one in the current IEEE 802.11 WLANs is not applicable because ACK mechanism is not implemented. This paper addresses this challenge by proposing ACK-less data rate adaptation methods by cap...

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires. As specific disturbances in on-wire deployments, we consider wind-forced perturbations and disturbances caused by impulsive forces to wires. Our contribution is to answer whether the historical positions/veloc...

This paper proposes a decentralized FL scheme for IoE devices connected via multi-hop networks. FL has gained attention as an enabler of privacy-preserving algorithms, but it is not guaranteed that FL algorithms converge to the optimal point because of non-convexity when using decentralized parameter averaging schemes. Therefore, a distributed algo...

In this paper, a stochasic geometry analysis of the inversely proportional setting (IPS) of carrier sense threshold (CST) and transmission power for densely deployed wireless local area networks (WLANs) is presented. In densely deployed WLANs, CST adjustment is a crucial technology to enhance spatial reuse, but it can starve surrounding transmitter...

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the ou...

This paper discusses the opportunity of bringing the concept of zero-shot adaptation into learning-based millimeter-wave (mmWave) communication systems, particularly in environments with unstable urban infrastructures. Here, zero-shot adaptation implies that a learning agent adapts to unseen scenarios during training without any adaptive fine-tunin...

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires, including wind-forced perturbations and disturbances caused by impulsive forces to wires. Our main contribution is to answer whether or not historical positions and velocities of a mmWave node is useful to tra...

The full text is available at: https://www.researchgate.net/publication/340644222_Differentially_Private_AirComp_Federated_Learning_with_Power_Adaptation_Harnessing_Receiver_Noise

This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machine learning (ML) model using the rich data and computational resources of mobile clients without collecting their data in central systems. Conventional FL with client sele...

Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications, especially at higher frequencies (e.g., millimeter-wave and terahertz technologies), through predictive handover and beamforming to solve line-of-sight (LOS) blockage problem. In this st...

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the ou...

The goal of this work is the accurate prediction of millimeter-wave received power leveraging both radio frequency (RF) signals and heterogeneous visual data from multiple distributed cameras, in a communication and energy-efficient manner while preserving data privacy. To this end, firstly focusing on data privacy, we propose heteromodal split lea...

Stochastic geometry analysis of wireless backhaul networks with beamforming in roadside environments is provided. In particular, a new model to analyze antenna gains, interference, and coverage in roadside environments of wireless networks with Poisson point process deployment of BSs is proposed. The received interference from the BSs with wired ba...

This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients without gathering their data to ce...

This paper proposes a method to predict received power in urban area deterministically, which can learn a prediction model from small amount of measurement data by a simulation-aided transfer learning and data augmentation. Recent development in machine learning such as artificial neural network (ANN) enables us to predict radio propagation and pat...

Federated learning (FL) enables a neural network (NN) to be trained using privacy-sensitive data on mobile devices while retaining all the data on their local storages. However, FL asks the mobile devices to perform heavy communication and computation tasks, i.e., devices are requested to upload and download large-volume NN models and train them. T...

Over-the-air computation (AirComp)-based federated learning (FL) enables low-latency uploads and the aggregation of machine learning models by exploiting simultaneous co-channel transmission and the resultant waveform superposition. This study aims at realizing secure AirComp-based FL against various privacy attacks where malicious central servers...

This paper proposes a robust adversarial reinforcement learning (RARL)-based multi-access point (AP) coordination method that is robust even against unexpected decentralized operations of uncoordinated APs. Multi-AP coordination is a promising technique towards IEEE 802.11be, and there are studies that use RL for multi-AP coordination. Indeed, a si...

This paper proposes a radio channel selection algorithm based on a contextual multi-armed bandit (CMAB) for a wireless local area network (WLAN) environment, in which the access probability of each access point (AP) and the throughput model are not given in advance. The problem to be considered inherently involves the exploration to obtain the know...

The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split...

The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split...

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels....

We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by ran...

Despite the widespread popularity of stochastic geometry analysis for cellular networks, most analytical results lack the perspective of channel-adaptive user scheduling. This study presents a stochastic geometry analysis of the SINR distribution and scheduling gain of normalized SNR-based scheduling in an uplink Poisson cellular network, in which...

For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance—e.g., the cumulative sum of time-varying data rates—proactively to avoid making myopic decisions. However, this study...

This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability given transmitter-side interference. A transmission probability depends on the opportunity prediction, preventing indiscriminate transmissions and reducing excessive int...

Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural...

In this paper, a reinforcement learning-based spatial reuse scheme for wireless local area networks (WLANs) is proposed and analyzed. In this scheme, when an access point (or a station) overhears an on-going transmission, it decodes the information in the frame header to identify the transmitter and decides whether or not to exploit spatial reuse a...

This paper discusses the impact of spatial reuse and carrier sense threshold (CST) optimization on the performance of wireless local area networks using stochastic geometry analysis. The adjustment of the CST is a promising approach to improve spatial reuse, and has been proposed for the IEEE 802.11ax standard. Considering the situation where each...

Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural...

This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability given transmitter-side interference. A transmission probability depends on the opportunity prediction, preventing indiscriminate transmissions, and reducing excessive in...

The modification of discrete bacterial foraging optimization algorithm using fuzzy system is presented in this study along with the application in femtocell networks to find cell and resource blocks that can provide connection with highest throughput. Fuzzy system is employed to guide reproduction events in discrete bacterial foraging optimization...

This study demonstrates the feasibility of proactive received power prediction by leveraging spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) networks. As the received power on a mmWave link can attenuate aperiodically owing to human blockages, a long-term series of the future received power cannot be predicted by...

This paper presents a stochastic geometry analysis of radio interference and a grid-based design of a primary exclusive region (PER) for spectrum sharing in 3D unmanned aerial vehicle (UAV) networks. When a UAV network shares frequency bands with a primary system (e.g., a weather radar system), the UAVs must avoid harmful interference with the prim...

For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that directly maps images to a handover decision. In the framework, camera images are utilized to allow for the prediction of blockage effects in a mmWave link, whereby a network co...

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channe...

A decentralized learning mechanism, Federated Learning (FL), has attracted much attention, which enables privacy-preserving training using the rich data and computational resources of mobile clients. However, data on mobile clients is typically not independent and identically distributed (IID) owing to diverse of mobile users' interest and usage, a...

Sharing perceptual data (e.g., camera and LiDAR data) with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communicatio...

In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, particularly in the early diffusion phase of mmWave-available vehicles, where not all vehicles havemmWave communication devices. This paper proposes a distributed position control method to establish long relay...

For mmWave networks, this paper proposes an image-to-decision proactive handover framework, which directly maps camera images to a handover decision. With the help of camera images, the proposed framework enables a proactive handover, i.e., a handover is triggered before a temporal variation in the received power induced by obstacles even if the va...

Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimet...

In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, especially in the early diffusion phase of mmWave-available vehicles, where not all the vehicles have mmWave communication devices. This paper proposes a distributed position control method for autonomous vehicl...