arXiv subjects

Tien Ngoc Ha

Publications and source records attributed to Tien Ngoc Ha.

6 recordsLinked to original sources

arXiv2025

Radio Map Estimation: Empirical Validation and Analysis

Radio maps provide metrics such as the received signal strength at every location in a geographical region of interest. Extensive research has been carried out in this context, but it relies almost exclusively on synthetic-data experiments. Thus, the practical aspects of the radio map estimation (RME) problem as well as the performance of existing estimators in the real world remain unknown. To fill this gap end, this paper puts forth the first comprehensive, rigorous, and reproducible study of RME with real data. The main contributions include (C1) an assessment of the viability of RME based on the estimation error that can be achieved, (C2) the analysis of the main phenomena and trade-offs involved in RME, including the experimental verification of theoretical findings in the literature, and (C3) a thorough evaluation of a wide range of estimators on realworld data. Remarkably, this reveals that the performance gain of existing deep estimators in their pure form may not compensate for their complexity. A simple enhancement (C4) is proposed to alleviate this issue. The vast amount of data collected for this study is published along with the developed simulator to enable research on new schemes, hopefully bringing RME one step closer to practical deployment.

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arXiv2025

Bayesian Radio Map Estimation: Fundamentals and Implementation via Diffusion Models

Radio map estimation (RME) is the problem of inferring the value of a certain metric (e.g. signal power) across an area of interest given a collection of measurements. While most works tackle this problem from a purely non-Bayesian perspective, some Bayesian estimators have been proposed. However, the latter focus on estimating the map itself, the Bayesian standpoint is adopted mainly to exploit prior information or to capture uncertainty. This paper pursues a more general formulation, where the goal is to determine the posterior distribution of the map given the measurements. Besides handling uncertainty and allowing standard Bayesian estimates, solving this problem is seen to enable minimum mean square error estimation of arbitrary map functionals (e.g. capacity, bit error rate, or coverage area to name a few) while training only for power estimation. A general Bayesian estimator is proposed based on conditional diffusion models and both the Bayesian and non-Bayesian paradigms are compared analytically and numerically to determine when the Bayesian approach is preferable.

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arXiv2024

Theoretical Analysis of the Radio Map Estimation Problem

Radio maps provide radio frequency metrics, such as the received signal strength, at every location of a geographic area. These maps, which are estimated using a set of measurements collected at multiple positions, find a wide range of applications in wireless communications, including the prediction of coverage holes, network planning, resource allocation, and path planning for mobile robots. Although a vast number of estimators have been proposed, the theoretical understanding of the radio map estimation (RME) problem has not been addressed. The present work aims at filling this gap along two directions. First, the complexity of the set of radio map functions is quantified by means of lower and upper bounds on their spatial variability, which offers valuable insight into the required spatial distribution of measurements and the estimators that can be used. Second, the reconstruction error for power maps in free space is upper bounded for three conventional spatial interpolators. The proximity coefficient, which is a decreasing function of the distance from the transmitters to the mapped region, is proposed to quantify the complexity of the RME problem. Numerical experiments assess the tightness of the obtained bounds and the validity of the main takeaways in complex environments.

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arXiv2024

Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty

Next generation communication systems require accurate beam alignment to counteract the impairments that characterize propagation in high-frequency bands. The overhead of the pilot sequences required to select the best beam pair is prohibitive when codebooks contain a large number of beams, as is the case in practice. To remedy this issue, some schemes exploit information about the user location to predict the best beam pair. However, these schemes (i) involve no measurements whatsoever, which generally results in a highly suboptimal predicted beam, and (ii) are not robust to localization errors. To address these limitations, this paper builds upon the notion of radio map to develop two algorithms that attain a balance between the quality of the obtained beam pair and measurement overhead. The proposed algorithms predict the received power corresponding to each pair and measure just the Q pairs with highest prediction. While the first algorithm targets simplicity, the second one relies on a Bayesian approach to endow the prediction process with robustness to localization error. The performance of both algorithms is shown to widely outperform existing methods using ray-tracing data.

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arXiv2024

Spoofing Detection in the Physical Layer with Graph Neural Networks

In a spoofing attack, a malicious actor impersonates a legitimate user to access or manipulate data without authorization. The vulnerability of cryptographic security mechanisms to compromised user credentials motivates spoofing attack detection in the physical layer, which traditionally relied on channel features, such as the received signal strength (RSS) measured by spatially distributed receivers or access points. However, existing methods cannot effectively cope with the dynamic nature of channels, which change over time as a result of user mobility and other factors. To address this limitation, this work builds upon the intuition that the temporal pattern of changes in RSS features can be used to detect the presence of concurrent transmissions from multiple (possibly changing) locations, which in turn indicates the existence of an attack. Since a localization-based approach would require costly data collection and would suffer from low spatial resolution due to multipath, the proposed algorithm employs a deep neural network to construct a graph embedding of a sequence of RSS features that reflects changes in the propagation conditions. A graph neural network then classifies these embeddings to detect spoofing attacks. The effectiveness and robustness of the proposed scheme are corroborated by experiments with real-data.

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arXiv2023

Spoofing Attack Detection in the Physical Layer with Robustness to User Movement

In a spoofing attack, an attacker impersonates a legitimate user to access or modify data belonging to the latter. Typical approaches for spoofing detection in the physical layer declare an attack when a change is observed in certain channel features, such as the received signal strength (RSS) measured by spatially distributed receivers. However, since channels change over time, for example due to user movement, such approaches are impractical. To sidestep this limitation, this paper proposes a scheme that combines the decisions of a position-change detector based on a deep neural network to distinguish spoofing from movement. Building upon community detection on graphs, the sequence of received frames is partitioned into subsequences to detect concurrent transmissions from distinct locations. The scheme can be easily deployed in practice since it just involves collecting a small dataset of measurements at a few tens of locations that need not even be computed or recorded. The scheme is evaluated on real data collected for this purpose.

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