Yusuke Koda

Kyoto University | Kyodai

With advancements in distributed autonomous systems (e.g., vehicles, sensors, and robots) in the 5G/6G era, sidelink communication technology has evolved as a distributed communication system in the third-generation partnership project (3GPP). However, the current sidelink communication design focusing on information dissemination or point-to-point...

To meet increasing demands for higher-rate communication, exploring the sub-terahertz (THz) band is attracting huge attention. The first standardization of a sub-THz communication was performed at the IEEE 802.15.3d task group, which designed an ultra-wideband short-range communication operating in a 300 GHz band. However, to alleviate hardware cha...

p>This study aims to provide a unified view of the various standard millimeter-wave (mmWave) channel modeling frameworks for mmWave wireless deployments in sixth-generation (6G) wireless networks, focusing on wireless personal area networks (WPAN), wireless local area networks (WLAN), and cellular networks (CN). The 6G era will witness the emergenc...

This paper proposes a comprehensive 3GPPcnaompatible channel model with statistical enhancement tailored for indoor short-range device-to-device (D2D) communications operating in the frequency range (FR) of 52.6-71.0 GHz termed FR2-2. Regardless of the existence of various channel models at this band for indoor communications, there will be a need...

p>With advancements in distributed autonomous systems (e.g., vehicles, sensors, and robots) in the 5G/6G era, sidelink communication technology has evolved as a distributed communication system in the third generation partnership project (3GPP). However, the current low-rate point-to-point sidelink communication design is not suitable for rapid dev...

p>With advancements in distributed autonomous systems (e.g., vehicles, sensors, and robots) in the 5G/6G era, sidelink communication technology has evolved as a distributed communication system in the third generation partnership project (3GPP). However, the current low-rate point-to-point sidelink communication design is not suitable for rapid dev...

p>This study conducts a wideband multi-path propagation measurement at the 95 GHz sub-terahertz band for short-range communication in a conference room desktop scenario. Regardless of the fact that the current 3rd generation partnership (3GPP) stochastic channel model (SCM) targets the frequency up to 100 GHz for various scenarios, neither detailed...

p>This study conducts a wideband multi-path propagation measurement at the 95 GHz sub-terahertz band for short-range communication in a conference room desktop scenario. Regardless of the fact that the current 3rd generation partnership (3GPP) stochastic channel model (SCM) targets the frequency up to 100 GHz for various scenarios, neither detailed...

p>This paper performs a first wideband indoor channel measurement at the 105 GHz sub-terahertz (sub-THz) band and analyzes the multipath characteristics in terms of the omnidirectional path-loss and angular characteristics. The measurement campaigns with the 4 GHz bandwidth are performed focusing on indoor short-range communication scenarios in a c...

p>This paper performs a first wideband indoor channel measurement at the 105 GHz sub-terahertz (sub-THz) band and analyzes the multipath characteristics in terms of the omnidirectional path-loss and angular characteristics. The measurement campaigns with the 4 GHz bandwidth are performed focusing on indoor short-range communication scenarios in a c...

p>This paper performs a first wideband indoor channel measurement at the 105 GHz sub-terahertz (sub-THz) band and analyzes the multipath characteristics in terms of the omnidirectional path-loss and angular characteristics. The measurement campaigns with the 4 GHz bandwidth are performed focusing on indoor short-range communication scenarios in a c...

Towards flexible channel measurements of millimeter wave (mmWave) communications, this paper proposes a time-alignment algorithm of multiple power delay profiles (PDPs) separately measured with different antenna rotation angles. To characterize mmWave channels, capturing both delay time and angular property of multi-path components is necessary, wh...

This paper first summarizes the specifications of standardized wireless personal area networks (WPANs) and wireless local-area networks (WLANs) using the 60 GHz band, for example IEEE 802.15.3c and 802.11ad standards, and the significance of the standardization, together with the standardization transition of third-generation partnership project (3...

This study proposes a 3GPP-compatible channel generation framework for ultra-wideband and indoor shortrange communication realized by the fifth-generation (5G) new radio operating in the frequency range (FR) of 52.6-71.0 GHz. The addition of this band, coined “FR2-2”, to the 5G specification allows foreseeing the use cases of shorter-range communic...

Millimeter wave (mmWave) multi-path channel models based on a Saleh-Valenzuela (SV) model are fundamental for simple link-level simulations in mmWave communication systems. However, parameter extractions of the model incur developer-dependent and sometimes manual procedures, which hinders from not only confirming the validity of the existing parame...

p>The study aims to provide a unified view of the various standard millimeter-wave (mmWave) channel modeling frameworks for mmWave and terahertz (THz) wireless deployments in sixth-generation (6G) networks. The 6G era will witness the emergence of security-sensitive, more mission-critical, and data-intensive applications, wherein massive amount of...

p>The study aims to provide a unified view of the various standard millimeter-wave (mmWave) channel modeling frameworks for mmWave and terahertz (THz) wireless deployments in sixth-generation (6G) networks. The 6G era will witness the emergence of security-sensitive, more mission-critical, and data-intensive applications, wherein massive amount of...

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...

Wireless channels can be inherently privacy preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-th...

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...

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 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...

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 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...

This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, enabling beyond-5G/6G mission-critical applications (autonomous/ remote-controlled vehicles, visuo-haptic virtual reality, and other cyber-physical applications). First, drawing on recent advances in machine learning and the avai...

Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-th...

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 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 article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of n...

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...

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...

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 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...

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...

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 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...

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...

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...

This paper discusses a measurement method of time-variant attenuation of IEEE 802.11ad wireless LAN signals in the 60 GHz band induced by human blockage. The IEEE 802.11ad access point (AP) transmits frames intermittently, not continuously. Thus, to obtain the time-varying signal attenuation, it is required to estimate the duration in which the AP...

This paper discusses the optimal decision-making for predictive handover in millimeter-wave (mmWave) commu- nication networks using information of pedestrian movement. In mmWave communication networks, human blockage causes significant performance degradation. Hence, to maximize the throughput, it might be important to perform a handover predictive...

This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks. The scheme provides the future RSS prediction of any mmWave links within the camera's view, including links where nodes are not transmitting frames. This enables network controll...

This paper presents a measurement of time-varying attenuation of IEEE802.11ad wireless LAN (WLAN) signals in 60GHz band induced by human blockage. The present measurement is a novel approach to obtain the attenuation, where a commercially available IEEE802.11ad access point (AP) and station (STA) are employed and the measurement is conducted under...