Yusuke Koda

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 experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation o

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 learning (MultSL), in which a large neural network (NN) is split into two wirelessly connected segments. The upper segment combines images and receiv

This paper discusses the optimal decision-making for predictive handover in millimeter-wave (mmWave) communication 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 predictively using information such as location and velocity of a pedestrian. To optimize the timing to perform the predictive handover, this paper presents a rei

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 network architecture that compresses the communication payload, i.e., images. Compared to a baseline solely utilizing RF signals, numerical results s

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 transmitted signals. To estimate whether the AP transmitted signals or not at each sampling point, this paper applies a simple two‐state hidden Markov

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/velocities of a mmWave node are useful to track directional beams, given the complicated on-wire dynamics. To this end, we implement deep reinforcement lea

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 communications with ultra-wide bandwidth. However, the current focus of the 3GPP channel model on longer-range communication hinders link-level simulations of

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 emergence of security-sensitive, more mission-critical, and data-intensive applications, wherein massive amount of data will be exchanged while satisfying the s

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 controller triggers a handover in a proactive fashion. Furthermore, direct mapping allows for the scalability of the number of pedestrians. This paper e

This paper reports 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 conference room, corridor, and office room, which have been considered in the primary 60 GHz communication systems standardized by the IEEE 802.15.3c/11ad

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 communication with a low rate is not suitable for rapid development of such autonomous systems. Instead, based on sidelink, developing distributed wi

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 parameters but also correcting cumulative knowledge about parameters for versatile environments. This study develops an end-to-end software to automatically

<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 conference room, corridor, and office room, which have been considered in the primary 60 GHz communication systems standardized by the IEEE 802.

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 learning with feature aggregation (HetSLAgg) that splits neural network (NN) models into camera-side and base station (BS)-side segments. The BS-side NN

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-the-air mixup ML (AirMixML). In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains