Ngoc Tien Ha

• Romero, Daniel; Ha, Tien Ngoc; Shrestha, Raju & Franceschetti, Massimo (2024). Theoretical Analysis of the Radio Map Estimation Problem. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 23(10), p. 13722–13737. doi: 10.1109/TWC.2024.3404022. Full text in Research Archive Show summary

  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.

• Romero, Daniel; Ha, Tien Ngoc & Gerstoft, Peter (2024). Spoofing attack detection in the physical layer with robustness to user movement. IEEE Wireless Communications and Networking Conference. ISSN 1525-3511. doi: 10.1109/WCNC57260.2024.10570909. Full text in Research Archive
• Romero, Daniel; Ha, Tien Ngoc; Shrestha, Raju & Franceschetti, Massimo (2024). Analytical Performance Bounds for Radio Map Estimation. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2024-Spring62846.2024.10683442. Full text in Research Archive
• Ha, Tien Ngoc; Romero, Daniel & Lopez-Valcarce, Roberto (2024). Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2024-Spring62846.2024.10683362. Full text in Research Archive
• Ha, Tien Ngoc & Romero, Daniel (2024). Spoofing Detection in the Physical Layer with Graph Neural Networks. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2024-Spring62846.2024.10683545. Full text in Research Archive
• Tran, Hanh T.M.; Nguyen, Hieu V.; Bui, Van-Phuc; Ha, Tien Ngoc; Nguyen, Van Tho & Nguyen, Duc-Hien [Show all 7 contributors for this article] (2024). Encoding reality with semantic interpretation in metaverse interactions. AEU - International Journal of Electronics and Communications. ISSN 1434-8411. 187. doi: 10.1016/j.aeue.2024.155512. Full text in Research Archive
• Shrestha, Raju; Ha, Tien Ngoc; Pham, Viet Quoc & Romero, Daniel (2023). Radio Map Estimation in the Real-World: Empirical Validation and Analysis, IEEE Conference on Antenna Measurements & Applications (CAMA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350323047. p. 169–174. doi: 10.1109/CAMA57522.2023.10352759. Full text in Research Archive Show summary

  Radio maps quantify received signal strength or other magnitudes of the radio frequency environment at every point of a geographical region. These maps play a vital role in a large number of applications such as wireless network planning, spectrum management, and optimization of communication systems. However, empirical validation of the large number of existing radio map estimators is highly limited. To fill this gap, a large data set of measurements has been collected with an autonomous unmanned aerial vehicle (UAV) and a representative subset of these estimators were evaluated on this data. The performance-complexity trade-off and the impact of fast fading are extensively investigated. Although sophisticated estimators based on deep neural networks (DNNs) exhibit the best performance, they are seen to require large volumes of training data to offer a substantial advantage relative to more traditional schemes. A novel algorithm that blends both kinds of estimators is seen to enjoy the benefits of both, thereby suggesting the potential of exploring this research direction further.

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