arXiv subjects

Koji Yamamoto

Publications and source records attributed to Koji Yamamoto.

At least 19 recordsLinked to original sources

arXiv2026

Wi-Fi Radar via Over-the-Air Referencing: Bridging Wi-Fi Sensing and Bistatic Radar

Wi-Fi channel state information (CSI), which is originally acquired for communication purposes, has recently been reused for sensing and radar-like functionalities. However, in practical Wi-Fi systems with independent clocks at the transmitter and receiver, the lack of a common delay and phase reference fundamentally precludes phase-coherent radar-like delay--Doppler analysis. By exploiting the line-of-sight (LoS) path component, i.e., the earliest-arriving direct path, as an over-the-air (OTA) reference for delay and phase, we propose an OTA LoS-path referencing scheme, termed LoSRef, that enables delay calibration and phase alignment under this practical constraint. Unlike conventional Wi-Fi bistatic radar systems that rely on wired reference signals or dedicated reference antennas, the proposed LoSRef-based framework enables phase-coherent bistatic radar-like operation that can be integrated into typically deployed Wi-Fi systems. Through human gait and respiration experiments in indoor environments, we demonstrate that phase-coherent channel impulse responses and corresponding delay--Doppler responses can be obtained using only commodity Wi-Fi devices. This enables physically interpretable human motion sensing, including gait-induced range variation and respiration-induced sub-wavelength displacement, as well as the extraction of target-induced dynamics up to 20 dB weaker than dominant static multipath components.

cs.NI↗

arXiv2026

Delay-Synchronous Wideband Channel Sounding Using Off-The-Shelf Multi-Antenna WiFi Devices

It has been shown that WiFi devices enable sensing of environments and targets through their channel state information. However, the same devices have not been used for delay-synchronous channel sounding due to challenges related to the stability of synchronization and lack of reference power levels. Due to factors such as uncertainty in symbol reception timing, impulse responses are discontinuous across acquisitions. The present paper addresses the challenges to perform delay-synchronous channel sounding using off-the-shelf multiple-antenna IEEE 802.11ax WiFi devices, referred to as SoundiFi. Stable delay synchronization and power level reference are realized by remoting the antennas with coaxial cables and devoting one of the antennas as a reference channel, with which the gain and delay of other simultaneous channels are defined. Indoor experiments confirmed that the impulse response becomes continuous across successive acquisitions and provide the absolute delay. The impulse response has a noise level at -115 dB, indicating the maximum path gain value that can be measured with the devices. The impulse response also revealed the existence of long-delayed multipaths up to 132 m propagation distance in a reverberant 30-m-long corridor.

eess.SP↗

arXiv2022

Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space

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 representative loss functions of ML tasks e.g, mean squared error (MSE) and Kullback-Leibler (KL) divergence, are convex functionals, algorithms that directly update functions in function spaces could converge to the optimal solution. The key concept of this paper is to tailor a consensus-based optimization algorithm to work in the function space and achieve the global optimum in a distributed manner. This paper first analyzes the convergence of the proposed algorithm in a function space, which is referred to as a meta-algorithm, and shows that the spectral graph theory can be applied to the function space in a manner similar to that of numerical vectors. Then, consensus-based multi-hop federated distillation (CMFD) is developed for a neural network (NN) to implement the meta-algorithm. CMFD leverages knowledge distillation to realize function aggregation among adjacent devices without parameter averaging. An advantage of CMFD is that it works even with different NN models among the distributed learners. Although CMFD does not perfectly reflect the behavior of the meta-algorithm, the discussion of the meta-algorithm's convergence property promotes an intuitive understanding of CMFD, and simulation evaluations show that NN models converge using CMFD for several tasks. The simulation results also show that CMFD achieves higher accuracy than parameter aggregation for weakly connected networks, and CMFD is more stable than parameter aggregation methods.

cs.NI↗

arXiv2022

Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations

It is known that deep neural networks (DNNs) classify an input image by paying particular attention to certain specific pixels; a graphical representation of the magnitude of attention to each pixel is called a saliency-map. Saliency-maps are used to check the validity of the classification decision basis, e.g., it is not a valid basis for classification if a DNN pays more attention to the background rather than the subject of an image. Semantic perturbations can significantly change the saliency-map. In this work, we propose the first verification method for attention robustness, i.e., the local robustness of the changes in the saliency-map against combinations of semantic perturbations. Specifically, our method determines the range of the perturbation parameters (e.g., the brightness change) that maintains the difference between the actual saliency-map change and the expected saliency-map change below a given threshold value. Our method is based on activation region traversals, focusing on the outermost robust boundary for scalability on larger DNNs. Experimental results demonstrate that our method can show the extent to which DNNs can classify with the same basis regardless of semantic perturbations and report on performance and performance factors of activation region traversals.

cs.CV↗

arXiv2022

Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach

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 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 recomposite CSI and that this could be compensated by camera images. To leverage the camera images, we use multimodal deep learning, where the two modalities, i.e., images and BFMs, are integrated to recomposite the CSI. We conducted experiments using IEEE 802.11ac devices. The experimental results confirmed that the recomposition accuracy of the proposed multimodal framework is improved compared to the single-modal framework only using images or BFMs.

eess.SP↗

arXiv2022

Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study

This study experimentally validated the possibility of angle of departure (AoD) estimation 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, which does not require a pre-obtained database. This contrasts with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, the BFF-based MUSIC affords an alternative AoD estimation method without access to channel state information (CSI). Specifically, the extensive experimental and numerical evaluations demonstrated that the BFF-based MUSIC successfully estimates the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study revealed that the BFF-based MUSIC achieved a comparable error of AoD estimation to the CSI-based MUSIC, while BFF is a highly compressed version of CSI in IEEE 802.11ac/ax.

eess.SP↗

arXiv2021

Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks

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 communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks.

cs.LG↗

arXiv2021

Computer Vision-assisted Single-antenna and Single-anchor RSSI Localization Harnessing Dynamic Blockage Events

This paper demonstrates the feasibility of single-antenna and single-RF (radio frequency)- anchor received power strength indicator (RSSI) localization (SARR-LOC) with the assistance of the computer vision (CV) technique. Generally, to perform radio frequency (RF)-based device localization, either 1) fine-grained channel state information or 2) RSSIs from multiple antenna elements or multiple RF anchors (e.g., access points) is required. Meanwhile, owing to deficiency of single-antenna and single-anchor RSSI, which only indicates a coarse-grained distance information between a receiver and a transmitter, realizing localization with single-antenna and single-anchor RSSI is challenging. Our key idea to address this challenge is to leverage CV technique and to estimate the most likely first Fresnel zone (FFZ) between the receiver and transmitter, where the role of the RSSI is to detect blockage timings. Specifically, historical positions of an obstacle that dynamically blocks the FFZ are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the FFZ boundaries, enabling the estimation of the FFZ and localization of the transmitter. The experimental evaluation revealed that the proposed SARR-LOC achieved the localization error less than 1.0 m in an indoor environment, which is comparable to that of a conventional triangulation-based RSSI localization with multiple RF anchors.

cs.MM↗

arXiv2021

Penalized and Decentralized Contextual Bandit Learning for WLAN Channel Allocation with Contention-Driven Feature Extraction

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 robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in the disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with joint a linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence -- the channel exploration cycle -- by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.

cs.NI↗

arXiv2021

Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing

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.

cs.LG↗

arXiv2021

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

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 skeptical point of view, the contribution of this study is twofold. First, by considering an example scenario, we confirm that the training-test gap adversely affects the beam-tracking performance. More specifically, we consider nodes placed on overhead messenger wires, where the node dynamics are affected by several environmental parameters, e.g, the wire mass and tension. Although these are particular scenarios, they yield insight into the validation of the training-test gap problems. Second, we demonstrate the feasibility of \textit{zero-shot adaptation} as a solution, where a learning agent adapts to environmental parameters unseen during training. This is achieved by leveraging a robust adversarial reinforcement learning (RARL) technique, where such training-and-test gaps are regarded as disturbances by adversaries that are jointly trained with a legitimate beam-tracking agent. Numerical evaluations demonstrate that the beam-tracking policy learned via RARL can be applied to a wide range of environmental parameters without severely degrading the received power.

cs.LG↗

arXiv2021

Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks

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, narrow-band and lossy IoT networks cause non-negligible packet losses and retransmissions, resulting in non-negligible communication latency. This study solves the problem of the incremental retransmission latency caused by packet loss in a lossy IoT network. We propose a split inference with no retransmissions (SI-NR) method that achieves high accuracy without any retransmissions, even when packet loss occurs. In SI-NR, the key idea is to train the ML model by emulating the packet loss by a dropout method, which randomly drops the output of hidden units in a DNN layer. This enables the SI-NR system to obtain robustness against packet losses. Our ML experimental evaluation reveals that SI-NR obtains accurate predictions without packet retransmission at a packet loss rate of 60%.

eess.SP↗

arXiv2021

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data

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 outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks. In DS-FL, the communication cost depends only on the output dimensions of the models and does not scale up according to the model size. The exchanged model outputs are used to label each sample of the open dataset, which creates an additionally labeled dataset. Based on the new dataset, local models are further trained, and model performance is enhanced owing to the data augmentation effect. We further highlight that in DS-FL, the heterogeneity of the devices' dataset leads to ambiguous of each data sample and lowing of the training convergence. To prevent this, we propose entropy reduction averaging, where the aggregated model outputs are intentionally sharpened. Moreover, extensive experiments show that DS-FL reduces communication costs up to 99% relative to those of the FL benchmark while achieving similar or higher classification accuracy.

cs.DC↗

arXiv2020

Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking

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 track directional beams given the complicated on-wire dynamics. To this end, we implement beam-tracking based on deep reinforcement learning (DRL) to learn the complicated relationships between the historical positions/velocities and appropriate beam steering angles. Our numerical evaluations yielded the following key insights: Against wind perturbations, an appropriate beam-tracking policy can be learned from the historical positions and velocities of a node. Meanwhile, against impulsive forces to the wire, the use of the position and velocity of the node is not necessarily sufficient owing to the rapid displacement of the node. To solve this, we propose to take advantage of the positional interaction on the wire by leveraging the positions/velocities of several points on the wire as state information in DRL. The results confirmed that this results in the avoidance of beam misalignment, which would not be possible by using only the position/velocity of the node.

cs.NI↗

arXiv2020

MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks

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 selection estimates the required time for an FL round from a given clients' computation power and throughput and determines a client set to reduce time consumption in FL rounds. However, it is difficult to obtain accurate resource information for all clients before the FL process is conducted because the available computation and communication resources change easily based on background computation tasks, background traffic, bottleneck links, etc. Consequently, the FL operator must select clients through exploration and exploitation processes. This paper proposes a multi-armed bandit (MAB)-based client selection method to solve the exploration and exploitation trade-off and reduce the time consumption for FL in mobile networks. The proposed method balances the selection of clients for which the amount of resources is uncertain and those known to have a large amount of resources. The simulation evaluation demonstrated that the proposed scheme requires less learning time than the conventional method in the resource fluctuating scenario.

cs.NI↗

arXiv2020

Online Trainable Wireless Link Quality Prediction System using Camera Imagery

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 study, a real-time online trainable wireless link quality prediction system was proposed; the system was implemented with commercially available laptops. The proposed system collects datasets, updates a model, and infers the received power in real-time. The experimental evaluation was conducted using 5 GHz Wi-Fi, where received signal strength could be degraded by 10 dB when the LOS path was blocked by large obstacles. The experimental results demonstrate that the prediction model is updated in real-time, adapts to the change in environment, and predicts the time-varying Wi-Fi received power accurately.

cs.NI↗

arXiv2020

Handover Management for mmWave Networks with Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

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 of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve time consecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion

cs.NI↗

arXiv2020

Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction

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 segment fuses RF signals and uploaded image features without collecting raw images. However, the usage of multiple visual data leads to an increase in NN input dimensions, which gives rise to additional communication and energy costs. To overcome additional communication and energy costs due to image interpolation to blend different frame rates, we propose a novel BS-side manifold mixup technique that offloads the interpolation operations from cameras to a BS. Subsequently, we confront energy costs for operating a larger size of the BS- side NN segment due to concatenating image features across cameras and propose an energy-efficient aggregation method. This is done via a linear combination of image features instead of concatenating them, where the NN size is independent of the number of cameras. Comprehensive test-bed experiments with measured channels demonstrate that HetSLAgg reduces the prediction error by 44% compared to a baseline leveraging only RF received power. Moreover, the experiments show that the designed HetSLAgg achieves over 20% gains in terms of communication and energy cost reduction compared to several baseline designs within at most 1% of accuracy loss.

cs.NI↗