arXiv subjects

Koya Sato

Publications and source records attributed to Koya Sato.

11 recordsLinked to original sources

arXiv2026

Radio Map Construction with Post-Hoc Location Calibration under Quasi-Static Positioning Errors: Joint Estimation, Performance Bounds, and GNSS-Based Evaluation

Radio maps enable environment-aware wireless and Internet-of-Things applications and can be constructed from location-tagged received signal strength (RSS) measurements collected by mobile devices. In urban environments, temporally correlated GNSS errors can shift an entire sensing trajectory, causing systematic spatial misregistration that is not mitigated by collecting more measurements. This paper presents a radio-map construction framework that uses the radio measurements themselves to calibrate erroneous location tags after data collection. The dominant positioning error is modeled as a sensor-specific quasi-static offset, which is jointly estimated with radio-propagation parameters in a Gaussian process regression (GPR) framework by exploiting complementary spatial information from distance-dependent path loss and spatially correlated shadowing. We establish lower and upper bounds on the conditional Bayes risk and show that, under a translation-invariant trajectory model, trajectory information alone cannot identify the quasi-static offset, thereby motivating the use of RSS-derived spatial information for calibration. Numerical evaluations across propagation conditions show that the proposed method reduces the mean squared error (MSE) gap from ideal GPR to approximately $3.26\mathrm{dB}^2$, compared with about $10\mathrm{dB}^2$ for position-error-agnostic and noisy-input GPR baselines. Evaluation using positioning-error models derived from smartphone GNSS measurements shows that the proposed method outperforms a KF--RTS trajectory-smoothing baseline despite unmodeled time-varying positioning errors, remaining within approximately $5\mathrm{dB}^2$ of ideal GPR at the median MSE. These results demonstrate that RSS measurements can serve not only as observations for radio-map reconstruction but also as spatial cues for post-hoc calibration of imperfectly geotagged sensing data.

eess.SP↗

arXiv2026

OpenWaveLogger v2026 (OWL-v2026): an open source, low cost, easy to build, high performance logger for wave data measurements

Ocean wave models are critical for weather and climate forecasting, and accurate in-situ wave observations are essential for validating and improving these models. Open-source, community-driven buoys have democratized wave observations via telemetry in recent years, but these systems transmit only limited amounts of data. Full high-frequency time series, required to study detailed wave physics, can still in most cases only be collected in situ using data loggers. Yet open-source, low-cost logger solutions remain scarce compared to their telemetry-enabled counterparts. Here we present the Openlogartemis Wave Logger (OWL-v2026), an open-source, low-cost, easy-to-build, high-performance logger for wave data measurements. The OWL-v2026 is built from off-the-shelf components from the maker community, requiring only through-hole soldering for assembly, and totals approximately 220USD per unit. Custom firmware enables high-frequency, low-jitter logging of six-axis inertial measurement unit (IMU) data at 208 or 416Hz, and GNSS position and Doppler velocity at 10Hz, with Pulse Per Second (PPS) synchronization for accurate absolute UTC timestamping. We have successfully validated continuous logging over more than 10 days at 208Hz, a power consumption of approximately 80mA (approximately 20 days of autonomy with three D-cell lithium batteries), and absolute UTC timestamp accuracy typically better than 10ms. Though the OWL-v2026 is a purely technical contribution, it has the potential to substantially expand the availability and affordability of high-frequency in-situ wave time series, similar to how the OpenMetBuoy (OMB) (Rabault 2022) expanded the availability of telemetry-enabled wave observations and helped spark new developments in low-cost open-source buoys.

physics.geo-ph↗

arXiv2026

Scalable Base Station Configuration via Bayesian Optimization with Block Coordinate Descent

This paper proposes a scalable Bayesian optimization (BO) framework for dense base-station (BS) configuration design. BO can find an optimal BS configuration by iterating parameter search, channel simulation, and probabilistic modeling of the objective function. However, its performance is severely affected by the curse of dimensionality, thereby reducing its scalability. To overcome this limitation, the proposed method sequentially optimizes per-BS parameters based on block coordinate descent while fixing the remaining BS configurations, thereby reducing the effective dimensionality of each optimization step. Numerical results demonstrate that the proposed approach significantly outperforms naive optimization in dense deployment scenarios.

cs.IT↗

arXiv2025

Joint Ex-Post Location Calibration and Radio Map Construction under Biased Positioning Errors

This paper proposes a high-accuracy radio map construction method tailored for environments where location information is affected by bursty errors. Radio maps are an effective tool for visualizing wireless environments. Although extensive research has been conducted on accurate radio map construction, most existing approaches assume noise-free location information during sensing. In practice, however, positioning errors ranging from a few to several tens of meters can arise due to device-based positioning systems (e.g., GNSS). Ignoring such errors during inference can lead to significant degradation in radio map accuracy. This study highlights that these errors often tend to be biased when using mobile devices as sensors. We introduce a novel framework that models these errors together with spatial correlation in radio propagation by embedding them as tunable parameters in the marginal log-likelihood function. This enables ex-post calibration of location uncertainty during radio map construction. Numerical results based on practical human mobility data demonstrate that the proposed method can limit RMSE degradation to approximately 0.25-0.29 dB, compared with Gaussian process regression using noise-free location data, whereas baseline methods suffer performance losses exceeding 1 dB.

eess.SP↗

arXiv2025

Mitigating the Impact of Location Uncertainty on Radio Map-Based Predictive Rate Selection via Noisy-Input Gaussian Process

This paper proposes a predictive rate-selection framework based on Gaussian process (GP)-based radio map construction that is robust to location uncertainty. Radio maps are a promising tool for improving communication efficiency in 6G networks. Although they enable the design of location-based maximum transmission rates by exploiting statistical channel information, existing discussions often assume perfect (i.e., noiseless) location information during channel sensing. Since such information must be obtained from positioning systems such as global navigation satellite systems, it inevitably involves positioning errors; this location uncertainty can degrade the reliability of radio map-based wireless systems. To mitigate this issue, we introduce the noisy-input GP (NIGP), which treats location noise as additional output noise by applying a Taylor approximation of the function of interest. Numerical results demonstrate that the proposed NIGP-based design achieves more reliable transmission-rate selection than pure GP and yields higher throughput than path loss-based rate selection.

eess.SP↗

arXiv2025

Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression

This paper introduces a noise-tolerant computing method for over-the-air computation (AirComp) aimed at weighted averaging, which is critical in various Internet of Things (IoT) applications such as environmental monitoring. Traditional AirComp approaches, while efficient, suffer significantly in accuracy due to noise enhancement in the normalization by the sum of weights. Our proposed method allows nodes to adaptively truncate their weights based on the channel conditions, thereby enhancing noise tolerance. Applied to distributed Gaussian process regression (D-GPR), the method facilitates low-latency, low-complexity, and high-accuracy distributed regression across a range of signal-to-noise ratios (SNRs). We evaluate the performance of the proposed method in a radio map construction problem, which involves visualizing the radio environment based on limited sensing information and spatial interpolation. Numerical results show that our approach maintains computational accuracy in low-SNR scenarios and achieves performance close to ideal conditions in high SNR environments. In addition, a case study targeting a federated learning (FL) system demonstrates the potential of our proposed method in improving model aggregation accuracy, not only for D-GPR but also for FL systems.

eess.SP↗

arXiv2024

Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design

This study proposes an adaptive experimental design framework for a channel-simulation-based base station (BS) design that supports the joint optimization of transmission power and placement. We consider a system in which multiple transmitters provide wireless services over a shared frequency band. Our objective is to maximize the average throughput within an area of interest. System operators can design the system configurations prior to deployment by iterating them through channel simulations and updating the parameters. However, accurate channel simulations are computationally expensive; therefore, it is preferable to configure the system using a limited number of simulation iterations. We develop a solver for the problem based on Bayesian optimization (BO), a black-box optimization method. The numerical results demonstrate that our proposed framework can achieve 18-22% higher throughput performance than conventional placement and power optimization strategies.

cs.NI↗

arXiv2022

Over-the-Air Gaussian Process Regression Based on Product of Experts

This paper proposes a distributed Gaussian process regression (GPR) with over-the-air computation, termed AirComp GPR, for communication- and computation-efficient data analysis over wireless networks. GPR is a non-parametric regression method that can model the target flexibly. However, its computational complexity and communication efficiency tend to be significant as the number of data increases. AirComp GPR focuses on that product-of-experts-based GPR approximates the exact GPR by a sum of values reported from distributed nodes. We introduce AirComp for the training and prediction steps to allow the nodes to transmit their local computation results simultaneously; the communication strategies are presented, including distributed training based on perfect and statistical channel state information cases. Applying to a radio map construction task, we demonstrate that AirComp GPR speeds up the computation time while maintaining the communication cost in training constant regardless of the numbers of data and nodes.

eess.SP↗

arXiv2020

DyANE: Dynamics-aware node embedding for temporal networks

Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks -- rather than of the network structure itself -- with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random-walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes' epidemic states in a single instance of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.

physics.soc-ph↗

arXiv2020

Network-Density-Controlled Decentralized Parallel Stochastic Gradient Descent in Wireless Systems

This paper proposes a communication strategy for decentralized learning on wireless systems. Our discussion is based on the decentralized parallel stochastic gradient descent (D-PSGD), which is one of the state-of-the-art algorithms for decentralized learning. The main contribution of this paper is to raise a novel open question for decentralized learning on wireless systems: there is a possibility that the density of a network topology significantly influences the runtime performance of D-PSGD. In general, it is difficult to guarantee delay-free communications without any communication deterioration in real wireless network systems because of path loss and multi-path fading. These factors significantly degrade the runtime performance of D-PSGD. To alleviate such problems, we first analyze the runtime performance of D-PSGD by considering real wireless systems. This analysis yields the key insights that dense network topology (1) does not significantly gain the training accuracy of D-PSGD compared to sparse one, and (2) strongly degrades the runtime performance because this setting generally requires to utilize a low-rate transmission. Based on these findings, we propose a novel communication strategy, in which each node estimates optimal transmission rates such that communication time during the D-PSGD optimization is minimized under the constraint of network density, which is characterized by radio propagation property. The proposed strategy enables to improve the runtime performance of D-PSGD in wireless systems. Numerical simulations reveal that the proposed strategy is capable of enhancing the runtime performance of D-PSGD.

cs.NI↗

arXiv2017

How the nature of web services drives vocabulary creation in social tagging

A social tagging system allows users to add arbitrary strings, called "tags", on a shared resource to organize and manage information. The Yule--Simon process, which has shown the ability to capture the population dynamics of social tagging behavior, does not handle the mechanism of new vocabulary creation because it assumes that new vocabulary creation is a Poisson-like random process. In this research, we focus on the mechanism of vocabulary creation from the microscopic perspective and discuss whether it also follows the random process assumed in the Yule--Simon process. To capture the microscopic mechanism of vocabulary creation, we focus on the relationship between the number of tags used in the same entry and the local vocabulary creation rate. We find that the relationship is not the result of a simple random process, and differs between services. Furthermore, these differences depend on whether the user's tagging attitudes are private or open. These results provide the potential for a new index to identify the service's intrinsic nature.

cs.SI↗