Daniel Romero

• 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; 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; 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
• 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
• 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 (2022). Comments on "Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection". IEEE Transactions on Signal Processing. ISSN 1053-587X. 70, p. 2899–2900. doi: 10.1109/TSP.2022.3182161. Full text in Research Archive
• Elnourani, Mohamed; Deshmukh, Siddharth; Beferull-Lozano, Baltasar & Romero, Daniel (2022). Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels. IEEE Transactions on Vehicular Technology. ISSN 0018-9545. 71(3), p. 3075–3091. doi: 10.1109/TVT.2022.3145011. Full text in Research Archive Show summary

  Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-state-information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives.

• Romero, Daniel & Kim, Seung-Jun (2022). Radio Map Estimation: A data-driven approach to spectrum cartography. IEEE Signal Processing Magazine. ISSN 1053-5888. 39(6), p. 53–72. doi: 10.1109/MSP.2022.3200175. Full text in Research Archive
• 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.

• Shrestha, Raju; Romero, Daniel & Chepuri, Sundeep Prabhakar (2022). Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 22(1), p. 627–641. doi: 10.1109/TWC.2022.3197087. Full text in Research Archive Show summary

  Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous existing techniques construct radio maps from spatially distributed measurements, the locations of such measurements are predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.

• Cardenas, Juan Diego; Beferull-Lozano, Baltasar; Elnourani, Mohamed & Romero, Daniel (2022). RISK-AWARE PARTICLE FILTERING FOR STATE ESTIMATION IN RECIRCULATING AQUACULTURE SYSTEMS, 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665459068. p. 829–833. doi: 10.1109/IEEECONF56349.2022.10051993. Full text in Research Archive Show summary

  We design a sequential non-linear risk-aware estimator based on particle filtering to compute estimates and approximate the system state posterior distribution. For this purpose, we consider the risk given by the expected variance of the squared error between the system state and the estimate conditioned on the observations. We compare the proposed estimator with existing risk-neutral estimators in terms of error variance, mean squared error, and execution time per iteration. The comparison is carried out using a simulation of a Recirculating Aquaculture System, a case study for non-linear critical systems, where the performance in estimation for low probability events becomes an important aspect. Our simulation results demonstrate a competitive estimation performance while ensuring a lower risk. 1 1 The reader can find the code for the proposed estimators in github.com/uia-wisenet/Risk_Aware_MMSE_PF.

• 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.

• Ghari, Siavash Mollaebrahim; Romero, Daniel & Beferull-Lozano, Baltasar (2021). Fast Decentralized Linear Functions Via Successive Graph Shift Operators. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. ISSN 1520-6149. p. 5225–5229. doi: 10.1109/ICASSP39728.2021.9415098. Full text in Research Archive
• Jaiswal, Rahul Kumar & Romero, Daniel (2021). Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise, 2021 11th International Conference on Information Science and Technology (ICIST). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665412667. p. 39–47. doi: 10.1109/ICIST52614.2021.9440639. Full text in Research Archive Show summary

  Speech quality is degraded in the presence of background noise, which reduces the quality of experience (QoE) of the end-user and therefore motivates the usage of speech enhancement algorithms. A large number of approaches have been proposed in this context. However most of them have focused on the case where the noise is stationary, an assumption that seldom holds in practice. For instance, in mobile telephony, noise sources with a marked non-stationary spectral signature include vehicles, machines, and other speakers to name a few. On the other hand, the usage of frequency-domain information in existing algorithms for speech enhancement in non-stationary noise environments can be made more effective by leveraging the increased flexibility introduced by implicit Wiener filters, which allow the control of the spectral reconstruction of the speech signal through the adjustment of hyperparameters. To address these limitations, the present paper develops an algorithm that recursively estimates the noise power spectral density and reconstructs the target speech signal in the frequency domain by means of an implicit Wiener filter with judiciously selected hyperparameters. The recursive noise estimation approach relies on the past and the present power spectral values. To evaluate the performance of the speech enhancement algorithm, speech uttered by a male and a female speaker degraded by non-stationary noise produced e.g. by babbling, cars, street noise, trains, restaurants, and airport noise. To this end, the NOIZEUS corpus is used. Objective speech quality measures such as the log-likelihood ratio (LLR), the cepstral distance (CD), and the weighted spectral slope distance (WSS) are evaluated for the enhanced speech signals and compared to the conventional spectral subtraction method. Results demonstrate that the proposed algorithm provides consistent and improved enhancement performance with all tested noise types.

• Elnourani, Mohamed; Deshmukh, Siddharth; Beferull-Lozano, Baltasar & Romero, Daniel (2020). Robust Transmit Beamforming for Underlay D2D Communications on Multiple Channels, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728154787. doi: 10.1109/SPAWC48557.2020.9154329. Full text in Research Archive
• Teganya, Yves & Romero, Daniel (2020). Deep Completion Autoencoders for Radio Map Estimation. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 21(3), p. 1710–1724. doi: 10.1109/TWC.2021.3106154. Full text in Research Archive Show summary

  Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes rely on interpolation algorithms unable to learn from data. In contrast, this paper proposes a novel approach in which the spatial structure of propagation phenomena such as shadowing is learned beforehand from a data set with measurements in other environments. Relative to existing schemes, a significantly smaller number of measurements is therefore required to estimate a map with a prescribed accuracy. As an additional novelty, this is also the first work to estimate radio maps using deep neural networks. Specifically, a deep completion autoencoder architecture is developed to effectively exploit the manifold structure of this class of maps.

• Zaman, Bakht; Ramos, Luis Miguel Lopez; Romero, Daniel & Beferull-Lozano, Baltasar (2020). Online Topology Identification From Vector Autoregressive Time Series. IEEE Transactions on Signal Processing. ISSN 1053-587X. 69, p. 220–225. doi: 10.1109/TSP.2020.3042940. Full text in Research Archive Show summary

  Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multi-variate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios.

• Teganya, Yves & Romero, Daniel (2020). Data-Driven Spectrum Cartography via Deep Completion Autoencoders, ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728150895. p. 1–7. doi: 10.1109/ICC40277.2020.9149400. Full text in Research Archive Show summary

  Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. This paper proposes a novel approach to spectrum cartography where propagation phenomena are learned from data. The resulting algorithms can therefore construct a spectrum map from a significantly smaller number of measurements than existing schemes since the spatial structure of shadowing and other phenomena is previously learned from maps in other environments. Besides the aforementioned new paradigm, this is also the first work to perform spectrum cartography with deep neural networks. To exploit the manifold structure of spectrum maps, a deep network architecture is proposed based on completion autoencoders.

• Romero, Daniel; Shrestha, Raju; Teganya, Yves & Chepuri, Sundeep Prabhakar (2020). Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs, 2020 IEEE International Workshop on Machine Learning for Signal Processing. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728166629. doi: 10.1109/MLSP49062.2020.9231595. Full text in Research Archive Show summary

  Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning algorithm relies on a map uncertainty metric to collect measurements at those locations where they are more informative. An online Bayesian learning algorithm is developed to update the map estimate and uncertainty metric every time a new measurement is collected, which enables real-time operation.

• Romero, Daniel; Ghari, Siavash Mollaebrahim; Beferull-Lozano, Baltasar & Marco, Cesar Asensio (2020). Fast Graph Filters for Decentralized Subspace Projection. IEEE Transactions on Signal Processing. ISSN 1053-587X. 69. doi: 10.1109/TSP.2020.3038528. Full text in Research Archive Show summary

  A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal number of iterations. Building upon tools from graph signal processing, the problem is cast as the design of a graph filter which, in turn, is reduced to the design of a suitable graph shift operator. Exploiting the eigenstructure of the projection and shift matrices leads to an objective whose minimization yields approximately minimum-order graph filters. To cope with the fact that this problem is not convex, the present work introduces a novel convex relaxation of the number of distinct eigenvalues of a matrix based on the nuclear norm of a Kronecker difference. To tackle the case where there exists no graph filter capable of implementing a certain subspace projection with a given network topology, a second optimization criterion is presented to approximate the desired projection while trading the number of iterations for approximation error. Two algorithms are proposed to optimize the aforementioned criteria based on the alternating-direction method of multipliers. An exhaustive simulation study demonstrates that the obtained filters can effectively obtain subspace projections markedly faster than existing algorithms.

• Elnourani, Mohamed; Beferull-Lozano, Baltasar; Romero, Daniel & Deshmukh, Siddharth (2019). Reliable Underlay Device-to-Device Communications on Multiple Channels, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538665282. doi: 10.1109/SPAWC.2019.8815544. Full text in Research Archive
• Teganya, Yves; Romero, Daniel; Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2019). Location-free Spectrum Cartography. IEEE Transactions on Signal Processing. ISSN 1053-587X. 67(15), p. 4013–4026. doi: 10.1109/TSP.2019.2923151. Full text in Research Archive Show summary

  Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals. This phenomenon occurs especially in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built on this approach and offer a markedly improved estimation performance than those of the existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

• Ghari, Siavash Mollaebrahim; Romero, Daniel & Beferull-Lozano, Baltasar (2019). Fast Decentralized Linear Functions via Successive Graph Shift Operators. arXiv.org. ISSN 2331-8422. Full text in Research Archive Show summary

  We study decentralized designing of the graph shift operators to implement linear transformations between graph signals. Since this operator captures the local structure of the graph, the proposed method of this paper gives rise to decentralized linear network operators. Unfortunately, existing decentralized approaches either consider some special instances of linear transformations or confine themselves to some known graph shift operators reduced family of the designing linear transformations task. To remedy these limitations, this paper develops a framework for computing a wide class of linear transformations in a decentralized fashion by relying on the notion of graph shift operator. To this end, a set of successive graph shift operators is implemented to compute linear transformations in a small number of iterations (as fast as possible).

• Romero, Daniel & Leus, Geert (2019). Non-cooperative aerial base station placement via stochastic optimization. IEEE Xplore Digital Library. doi: 10.1109/MSN48538.2019.00036. Full text in Research Archive
• Ghari, Siavash Mollaebrahim; Asensio-Marco, César; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Decentralized subspace projection in large networks, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728112954. doi: 10.1109/GlobalSIP.2018.8646531. Full text in Research Archive
• Lopez-Ramos, Luis M.; Romero, Daniel; Zaman, Bakht & Beferull-Lozano, Baltasar (2018). Dynamic network identification from non-stationary vector autoregressive time series, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728112954. doi: 10.1109/GlobalSIP.2018.8646607. Full text in Research Archive
• Weerasinghe, Thilina Nuwan; Romero, Daniel; Asensio-Marco, César & Beferull-Lozano, Baltasar (2018). Fast Distributed Subspace Projection via Graph Filters, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538646588. p. 4639–4643. doi: 10.1109/ICASSP.2018.8462110. Full text in Research Archive Show summary

  A significant number of linear inference problems in wireless sensor networks can be solved by projecting the observed signal onto a given subspace. Decentralized approaches avoid the need for performing such an operation at a central processor, thereby reducing congestion and increasing the robustness and the scalability of the network. Unfortunately, existing decentralized approaches either confine themselves to a reduced family of subspace projection tasks or need an infinite number of iterations to obtain the exact projection. To remedy these limitations, this paper develops a framework for computing a wide class of subspace projections in a decentralized fashion by relying on the notion of graph filtering. To this end, a methodology to obtain the shift matrix and the corresponding filter coefficients that provide exact subspace projection in a nearly minimal number of iterations is proposed. Numerical experiments corroborate the merits of the proposed approach.

• Elnourani, Mohamed; Hamid, Mohamed; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Underlay Device-to-Device Communications on Multiple Channels, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538646588. p. 3684–3688. doi: 10.1109/ICASSP.2018.8462424. Full text in Research Archive Show summary

  Since the spectral efficiency of wireless communications is already close to its fundamental bounds, a significant increase in spatial efficiency is required to meet future traffic demands. Device-to-device (D2D) communications provide such an increase by allowing nearby users to communicate directly without passing their packages through the base station. To fully exploit the benefits of this paradigm, proper channel assignment and power allocation algorithms are required. The main limitation of existing schemes, which restrict D2D transmitters to operate on a single channel at a time, is circumvented by the joint channel assignment and power allocation algorithm proposed in this paper. This algorithm relies on convex relaxation to efficiently obtain nearlyoptimal solutions to the mixed-integer program arising in this context. Numerical experiments corroborate the merits of the proposed scheme relative to state-of-the art alternatives.

• Teganya, Yves; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Localization-Free Power Cartography, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538646588. p. 3549–3553. doi: 10.1109/ICASSP.2018.8461731. Full text in Research Archive Show summary

  Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using measurements of spatially distributed sensors. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radio to name a few. Existing spectrum cartography methods necessitate knowledge of sensor locations, but such locations cannot be accurately determined from pilot positioning signals (such as those in LTE or GPS) in indoor or dense urban scenarios due to multipath. To circumvent this limitation, this paper proposes localization-free cartography, where spectral maps are directly constructed from features of these positioning signals rather than from location estimates. The proposed algorithm capitalizes on the framework of kernel-based learning and offers improved prediction performance relative to existing alternatives, as demonstrated by a simulation study in a street canyon.

• Ioannidis, Vassilis N.; Romero, Daniel & Giannakis, Georgios B. (2018). Inference of Spatio-Temporal Functions Over Graphs via Multikernel Kriged Kalman Filtering. IEEE Transactions on Signal Processing. ISSN 1053-587X. 66(12), p. 3228–3239. doi: 10.1109/TSP.2018.2827328. Full text in Research Archive
• Ioannidis, Vassilis N.; Ma, Meng; Nikolakopoulos, Athanasios N.; Romero, Daniel & Giannakis, Georgios B. (2018). Kernel-Based Inference of Functions Over Graphs. In Comminiello, Danilo & Principe, Jose (Ed.), Adaptive Learning Methods for Nonlinear System Modeling. Elsevier. ISSN 9780128129760. doi: 10.1016/B978-0-12-812976-0.00010-5. Full text in Research Archive
• Ramirez, David; Romero, Daniel; Via, Javier; Lopez-Valcarce, Roberto & Santamaría, Ignacio (2018). Locally optimal invariant detector for testing equality of two power spectral densities. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. ISSN 1520-6149. 2018-April, p. 3929–3933. doi: 10.1109/ICASSP.2018.8462683. Full text in Research Archive
• Romero, Daniel; Lee, Donghoon & Giannakis, Georgios B. (2018). Blind Radio Tomography. IEEE Transactions on Signal Processing. ISSN 1053-587X. 66(8), p. 2055–2069. doi: 10.1109/TSP.2018.2799169. Full text in Research Archive
• Ramirez, David; Romero, Daniel; Via, Javier; Lopez-Valcarce, Roberto & Santamaría, Ignacio (2018). Testing Equality of Multiple Power Spectral Density Matrices. IEEE Transactions on Signal Processing. ISSN 1053-587X. 66(23), p. 6268–6280. doi: 10.1109/TSP.2018.2875884. Full text in Research Archive Show summary

  This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.

• Zhang, Liang; Wang, Gang; Romero, Daniel & Giannakis, GB (2017). Randomized Block Frank–Wolfe for Convergent Large-Scale Learning. IEEE Transactions on Signal Processing. ISSN 1053-587X. 65(24), p. 6448–6461. doi: 10.1109/TSP.2017.2755597. Full text in Research Archive
• Romero, Daniel; Kim, Seung-Jun; Giannakis, GB & Lopez-Valcarce, Roberto (2017). Learning Power Spectrum Maps From Quantized Power Measurements. IEEE Transactions on Signal Processing. ISSN 1053-587X. 65(10), p. 2547–2560. doi: 10.1109/TSP.2017.2666775. Full text in Research Archive
• Romero, Daniel; Ioannidis, Vassilis N. & Giannakis, GB (2017). Kernel-Based Reconstruction of Space-Time Functions on Dynamic Graphs. IEEE Journal on Selected Topics in Signal Processing. ISSN 1932-4553. 11(6), p. 856–869. doi: 10.1109/JSTSP.2017.2726976. Full text in Research Archive
• Lopez-Valcarce, Roberto & Romero, Daniel (2017). Defending Surveillance Sensor Networks Against Data-Injection Attacks via Trusted Nodes. European Signal Processing Conference. ISSN 2076-1465. p. 380–384. doi: 10.23919/EUSIPCO.2017.8081233. Full text in Research Archive
• Ioannidis, Vassilis N.; Romero, Daniel & Giannakis, Georgios B. (2017). Inference of spatiotemporal processes over graphs via kernel kriged Kalman filtering. In Maragos, P (Eds.), 2017 25th European Signal Processing Conference (EUSIPCO). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9780992862671. p. 1679–1683. doi: 10.23919/EUSIPCO.2017.8081495. Full text in Research Archive
• Giannakis, GB; Romero, Daniel & Ma, Meng (2017). Kernel-Based Reconstruction of Graph Signals. IEEE Transactions on Signal Processing. ISSN 1053-587X. p. 764–778. doi: 10.1109/TSP.2016.2620116. Full text in Research Archive
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2017). Online topology estimation for vector autoregressive processes in data networks. In N, N. (Eds.), Computational Advances in Multi-Sensor Adaptive Processing, IEEE 7th International Workshop on (CAMSAP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538612514. doi: 10.1109/CAMSAP.2017.8313211. Full text in Research Archive Show summary

  An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.

• Dony, Ariananda Dyonisius; Romero, Daniel & Leus, Geert (2017). Cooperative Compressive Power Spectrum Estimation in Wireless Fading Channels, 2017 International Conference on Electrical Engineering and Informatics (ICELTICs 2017). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538629321. doi: 10.1109/ICELTICS.2017.8253254. Full text in Research Archive

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• Cardenas-Cartagena, Juan; Elnourani, Mohamed; Beferull-Lozano, Baltasar & Romero, Daniel (2022). Risk-Aware Particle-Filtering for State Estimation in Recirculating Aquaculture Systems. Full text in Research Archive
• Cardenas, Juan Diego; Beferull-Lozano, Baltasar; Elnourani, Mohamed & Romero, Daniel (2022). RISK-AWARE PARTICLE FILTERING FOR STATE ESTIMATION IN RECIRCULATING AQUACULTURE SYSTEMS. Full text in Research Archive
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2019). Online Topology Identification from Vector Autoregressive Time Series. Full text in Research Archive Show summary

  Due to their capacity to condense the spatiotemporal structure of a data set in a format amenable for human inter-pretation, forecasting, and anomaly detection, causality graphsare routinely estimated in social sciences, natural sciences, andengineering. A popular approach to mathematically formalizecausality is based on vector autoregressive (VAR) models, whichconstitutes an alternative to the well-known but usually in-tractable Granger causality. Relying on such a VAR causalitynotion, this paper develops two algorithms with complementarybenefits to track time-varying causality graphs in an onlinefashion. Despite using data in a sequential fashion, both al-gorithms are shown to asymptotically attain the same averageperformance as a batch estimator with all data available atonce. Moreover, their constant complexity per update rendersthese algorithms appealing for big-data scenarios. Theoreticaland experimental performance analysis support the merits ofthe proposed algorithms. Remarkably, no probabilistic modelsor stationarity assumptions need to be introduced, which endows the developed algorithms with considerable generality.

• Lopez-Ramos, Luis M.; Romero, Daniel; Zaman, Bakht & Beferull-Lozano, Baltasar (2018). Dynamic network identification from non-stationary vector autoregressive time series. Full text in Research Archive
• Elnourani, Mohamed; Hamid, Mohamed; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Underlay Device-to-Device Communications on Multiple Channels. Full text in Research Archive
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2017). Online Topology Estimation for Vector Autoregressive Processes in Data Networks. Full text in Research Archive
• Elnourani, Mohamed; Beferull-Lozano, Baltasar & Romero, Daniel (2020). Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications. Universitetet i Agder. ISSN 9788271179953. Full text in Research Archive Show summary

  Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding. We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis. Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs. The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability. All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives.

• Teganya, Yves; Romero, Daniel & Beferull-Lozano, Baltasar (2020). Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels. Universitetet i Agder. ISSN 9788271179991. Full text in Research Archive Show summary

  In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.

• Zaman, Bakht; Beferull-Lozano, Baltasar; Romero, Daniel & Lopez-Ramos, Luis M. (2020). Online Machine Learning for Graph Topology Identification from Multiple Time Series. Universitetet i Agder. ISSN 9788284270005. Full text in Research Archive Show summary

  High dimensional time series data are observed in many complex systems. In networked data, some of the time series are influenced by other time series. Identifying these relations encoded in a graph structure or topology among the time series is of paramount interest in certain applications since the identified structure can provide insights about the underlying system and can assist in inference tasks. In practice, the underlying topology is usually sparse, that is, not all the participating time series in influence each other. The goal of this dissertation pertains to study the problem of sparse topology identification under various settings. Topology identification from time series is a challenging task. The first major challenge in topology identification is that the assumption of static topology does not hold always in practice since most of the practical systems are evolving with time. For instance, in econometrics, social networks, etc., the relations among the time series can change over time. Identifying the topologies of such dynamic networks is a major challenge. The second major challenge is that in most practical scenarios, the data is not available at once - it is coming in a streaming fashion. Hence, batch approaches are either not applicable or they become computationally expensive since a batch algorithm is needed to be run when a new datum becomes available. The third challenge is that the multi-dimensional time series data can contain missing values due faulty sensors, privacy and security reasons, or due to saving energy. We address the aforementioned challenges in this dissertation by proposing online/-batch algorithms to solve the problem of time-varying topology identification. A model based on vector autoregressive (VAR) process is adopted initially. The parameters of the VAR model reveal the topology of the underlying network. First, two online algorithms are proposed for the case of streaming data. Next, using the same VAR model, two online algorithms under the framework of online optimization are presented to track the time-varying topologies. To evaluate the performance of propose online algorithms, we show that both the proposed algorithms incur a sublinear static regret. To characterize the performance theoretically in time-varying scenarios, a bound on the dynamic regret for one of the proposed algorithms (TIRSO) is derived. Next, using a structural equation model (SEM) for topology identification, an online algorithm for tracking time-varying topologies is proposed, and a bound on the dynamic regret is also derived for the proposed algorithm. Moreover, using a non-stationary VAR model, an algorithm for dynamic topology identification and breakpoint detection is also proposed, where the notion of local structural breakpoint is introduced to accommodate the concept of breakpoint where instead of the whole topology, only a few edges vary. Finally, the problem of tracking VAR-based time-varying topologies with missing data is investigated. Online algorithms are proposed where the joint signal and topology estimation is carried out. Dynamic regret analysis is also presented for the proposed algorithm. For all the previously mentioned works, simulation tests about the proposed algorithms are also presented and discussed in this dissertation. The numerical results of the proposed algorithms corroborate with the theoretical analysis presented in this dissertation.

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