Viet Quoc Pham

• Pham, Viet Quoc & Romero, Daniel (2026). Path Planning for Aerial Relays via Probabilistic Roadmaps. IEEE Transactions on Communications. ISSN 0090-6778. 74, p. 4251–4266. doi: 10.1109/tcomm.2026.3658381. Full text in Research Archive Show summary

  Autonomous unmanned aerial vehicles (UAVs) can be utilized as aerial relays to serve users far from terrestrial infrastructure. Unfortunately, existing algorithms for aerial relay path planning cannot accommodate general flight constraints or channel models. This is required in practice due to connectivity constraints, the presence of obstacles (e.g. buildings), and regulations. This paper proposes a framework that overcomes these limitations by spatially discretizing the flight region. To cope with the resulting exponential growth in complexity, the framework adopts a probabilistic roadmap approach, where a shortest path is found through a graph of randomly generated states. To attain high optimality with affordable complexity, the probability distribution used to generate these states is designed based on heuristic path planners with theoretical guarantees. The algorithms derived in this framework not only overcome the main limitations of existing schemes but also entail smaller computational complexity. Extensive theoretical and numerical results corroborate the merits of the proposed approach.

• Pham, Viet Quoc & Romero, Daniel (2025). Spatial Transformers for Radio Map Estimation. IEEE International Conference on Communications. ISSN 1550-3607. p. 6155–6160. doi: 10.1109/icc52391.2025.11160709. Full text in Research Archive Show summary

  Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations onto a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a very large number of parameters. The first contribution addresses these limitations by means of an attention-based estimator based on transformers, which are AI models that achieved widespread popularity since they are the technology behind chatbots such as ChatGPT. The proposed scheme, named Spatial TransfOrmer for Radio Map estimation (STORM), not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, by which the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real-measurement datasets.

• Romero, Daniel; Pham, Viet Quoc & Shrestha, Raju (2024). Aerial Base Station Placement via Propagation Radio Maps. IEEE Transactions on Communications. ISSN 0090-6778. 72(9), p. 5349–5364. doi: 10.1109/TCOMM.2024.3383111. Full text in Research Archive
• Pham, Viet Quoc & Romero, Daniel (2023). Probabilistic roadmaps for aerial relay path planning. IEEE Global Communications Conference (GLOBECOM). ISSN 2334-0983. doi: 10.1109/GLOBECOM54140.2023.10437427. Full text in Research Archive Show summary

  Unmanned aerial vehicles (UAVs) with on-board relays can be used to establish multi-hop links that deliver high-speed connectivity beyond cell limits. This is of utmost importance e.g. in remote areas and in emergency scenarios. However, jointly designing the trajectories of multiple such flying relays is a complex task since the dimensionality of the underlying configuration space is too large to allow the direct application of traditional shortest-path methods. To bypass this difficulty, this work proposes a probabilistic roadmap algorithm based on a novel heuristic path design which is guaranteed to provide feasible paths for all UAVs under general conditions. This addresses the limitations of existing algorithms, which are typically based on non-linear optimization and, therefore, entail high complexity and cannot readily accommodate the presence of obstacles such as buildings. As corroborated via numerical experiments in an urban environment, the proposed scheme can establish a high-speed link with a user by means of just two aerial relays in a short time.

• 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.

• Pham, Viet Quoc & Romero, Daniel (2022). Implicit channel charting with application to UAV-aided localization, 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665494557. doi: 10.1109/SPAWC51304.2022.9833966. Full text in Research Archive Show summary

  Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.

• Romero, Daniel; Pham, Viet Quoc & Leus, Geert (2022). Aerial base station placement leveraging radio tomographic maps, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665405409. p. 5358–5362. doi: 10.1109/ICASSP43922.2022.9746987. Full text in Research Archive Show summary

  Mobile base stations on board unmanned aerial vehicles (UAVs) promise to deliver connectivity to those areas where the terrestrial infrastructure is overloaded, damaged, or absent. A fundamental problem in this context involves determining a minimal set of locations in 3D space where such aerial base stations (ABSs) must be deployed to provide coverage to a set of users. While nearly all existing approaches rely on average characterizations of the propagation medium, this work develops a scheme where the actual channel information is exploited by means of a radio tomographic map. A convex optimization approach is presented to minimize the number of required ABSs while ensuring that the UAVs do not enter nofly regions. A simulation study reveals that the proposed algorithm markedly outperforms its competitors.

• Pham, Viet Quoc & Romero, Daniel (2022). Aerial Base Station Placement: A Tutorial Introduction. IEEE Communications Magazine. ISSN 0163-6804. 60(5), p. 44–49. doi: 10.1109/MCOM.001.2100861. Full text in Research Archive Show summary

  The deployment of aerial base stations (ABSs) mounted onboard unmanned aerial vehicles is emerging as a promising technology to provide connectivity in areas where terrestrial infrastructure is insufficient or absent. This may occur, for example, in remote areas, large events, emergency situations, or areas affected by natural disasters such as wildfires or tsunamis. To successfully materialize this goal, it is required that ABSs are placed at locations in 3D space that ensure a high quality of service to the ground terminals. This article provides a tutorial introduction to this ABS placement problem where the fundamental challenges and trade-offs are first investigated by means of a toy application example. Next, the different approaches in the literature to address the aforementioned challenges in both 2D or 3D space are introduced, and a discussion on adaptive placement is provided. The article is concluded by discussing future research directions.

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