Daniel Romero

• 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 979-8-3503-2304-7. s. 169–174. doi: 10.1109/CAMA57522.2023.10352759. Fulltekst i vitenarkiv Vis sammendrag

  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 (2023). Probabilistic roadmaps for aerial relay path planning. IEEE Global Communications Conference (GLOBECOM). ISSN 2334-0983. doi: 10.1109/GLOBECOM54140.2023.10437427. Vis sammendrag

  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.

• 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 conference proceedings. ISSN 978-1-6654-5906-8. s. 829–833. doi: 10.1109/IEEECONF56349.2022.10051993. Vis sammendrag

  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). Implicit channel charting with application to UAV-aided localization, 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). IEEE conference proceedings. ISSN 978-1-6654-9455-7. doi: 10.1109/SPAWC51304.2022.9833966. Vis sammendrag

  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 & Kim, Seung-Jun (2022). Radio Map Estimation: A data-driven approach to spectrum cartography. IEEE Signal Processing Magazine. ISSN 1053-5888. 39(6), s. 53–72. doi: 10.1109/MSP.2022.3200175.
• Shrestha, Raju; Romero, Daniel & Prabhakar Chepuri, Sundeep (2022). Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 22(1), s. 627–641. doi: 10.1109/TWC.2022.3197087. Vis sammendrag

  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.

• Pham, Viet Quoc & Romero, Daniel (2022). Aerial Base Station Placement: A Tutorial Introduction. IEEE Communications Magazine. ISSN 0163-6804. 60(5), s. 44–49. doi: 10.1109/MCOM.001.2100861. Vis sammendrag

  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.

• Romero, Daniel (2022). Comments on "Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection". IEEE Transactions on Signal Processing. ISSN 1053-587X. 70, s. 2899–2900. doi: 10.1109/TSP.2022.3182161.
• 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 conference proceedings. ISSN 978-1-6654-0540-9. s. 5358–5362. doi: 10.1109/ICASSP43922.2022.9746987. Vis sammendrag

  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.

• 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), s. 3075–3091. doi: 10.1109/TVT.2022.3145011. Vis sammendrag

  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.

• 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. s. 5225–5229. doi: 10.1109/ICASSP39728.2021.9415098.
• 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 conference proceedings. ISSN 978-1-6654-1266-7. s. 39–47. doi: 10.1109/ICIST52614.2021.9440639. Fulltekst i vitenarkiv
• Romero, Daniel; Shrestha, Raju; Teganya, Yves & Prabhakar Chepuri, Sundeep (2020). Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs, 2020 IEEE International Workshop on Machine Learning for Signal Processing. IEEE conference proceedings. ISSN 978-1-7281-6662-9. doi: 10.1109/MLSP49062.2020.9231595.
• Zaman, Bakht; Lopez Ramos, Luis Miguel; Romero, Daniel & Beferull-Lozano, Baltasar (2020). Online Topology Identification From Vector Autoregressive Time Series. IEEE Transactions on Signal Processing. ISSN 1053-587X. s. 220–225. doi: 10.1109/TSP.2020.3042940.
• 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. doi: 10.1109/TSP.2020.3038528.
• Romero, Daniel; Shrestha, Raju; Teganya, Yves & Prabhakar Chepuri, Sundeep (2020). Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs. Machine Learning for Signal Processing. ISSN 1551-2541. s. 1–6. doi: 10.1109/MLSP49062.2020.9231595. Vis sammendrag

  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.

• Teganya, Yves & Romero, Daniel (2020). Deep Completion Autoencoders for Radio Map Estimation. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 21(3), s. 1710–1724. doi: 10.1109/TWC.2021.3106154. Vis sammendrag

  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.

• 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 978-1-7281-5089-5. s. 1–7. doi: 10.1109/ICC40277.2020.9149400. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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 978-1-7281-5478-7. doi: 10.1109/SPAWC48557.2020.9154329. Fulltekst i vitenarkiv
• Romero, Daniel & Leus, Geert (2019). Non-cooperative aerial base station placement via stochastic optimization. IEEE Xplore Digital Library. doi: 10.1109/MSN48538.2019.00036.
• Ghari, Siavash Mollaebrahim; Romero, Daniel & Beferull-Lozano, Baltasar (2019). Fast Decentralized Linear Functions via Successive Graph Shift Operators. arXiv.org. ISSN 2331-8422. Vis sammendrag

  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).

• 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 conference proceedings. ISSN 978-1-5386-6528-2. doi: 10.1109/SPAWC.2019.8815544. Fulltekst i vitenarkiv
• 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), s. 4013–4026. doi: 10.1109/TSP.2019.2923151. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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 conference proceedings. ISSN 978-1-7281-1295-4. doi: 10.1109/GlobalSIP.2018.8646607. Fulltekst i vitenarkiv
• 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 conference proceedings. ISSN 978-1-7281-1295-4. doi: 10.1109/GlobalSIP.2018.8646531. Fulltekst i vitenarkiv
• Ioannidis, Vassilis N.; Ma, Meng; Nikolakopoulos, Athanasios N.; Romero, Daniel & Giannakis, Georgios B. (2018). Kernel-Based Inference of Functions Over Graphs. I Comminiello, Danilo & Principe, Jose (Red.), Adaptive Learning Methods for Nonlinear System Modeling. Elsevier. ISSN 9780128129760. doi: 10.1016/B978-0-12-812976-0.00010-5. Fulltekst i vitenarkiv
• 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, s. 3929–3933. doi: 10.1109/ICASSP.2018.8462683. Fulltekst i vitenarkiv
• 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), s. 6268–6280. doi: 10.1109/TSP.2018.2875884. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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 conference proceedings. ISSN 978-1-5386-4658-8. s. 4639–4643. doi: 10.1109/ICASSP.2018.8462110. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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 conference proceedings. ISSN 978-1-5386-4658-8. s. 3549–3553. doi: 10.1109/ICASSP.2018.8461731. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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 conference proceedings. ISSN 978-1-5386-4658-8. s. 3684–3688. doi: 10.1109/ICASSP.2018.8462424. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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), s. 3228–3239. doi: 10.1109/TSP.2018.2827328. Fulltekst i vitenarkiv
• Romero, Daniel; Lee, Donghoon & Giannakis, Georgios B. (2018). Blind Radio Tomography. IEEE Transactions on Signal Processing. ISSN 1053-587X. 66(8), s. 2055–2069. doi: 10.1109/TSP.2018.2799169. Fulltekst i vitenarkiv
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2017). Online topology estimation for vector autoregressive processes in data networks. I N, N. (Red.), Computational Advances in Multi-Sensor Adaptive Processing, IEEE 7th International Workshop on (CAMSAP). IEEE conference proceedings. ISSN 978-1-5386-1251-4. doi: 10.1109/CAMSAP.2017.8313211.
• Dyonisius Dony, Ariananda; 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 conference proceedings. ISSN 978-1-5386-2932-1. doi: 10.1109/ICELTICS.2017.8253254.
• Giannakis, GB; Romero, Daniel & Ma, Meng (2017). Kernel-Based Reconstruction of Graph Signals. IEEE Transactions on Signal Processing. ISSN 1053-587X. s. 764–778. doi: 10.1109/TSP.2016.2620116.
• Ioannidis, Vassilis N.; Romero, Daniel & Giannakis, Georgios B. (2017). Inference of spatiotemporal processes over graphs via kernel kriged Kalman filtering. I Maragos, P (Red.), 2017 25th European Signal Processing Conference (EUSIPCO). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-9928626-7-1. s. 1679–1683. doi: 10.23919/EUSIPCO.2017.8081495.
• Lopez-Valcarce, Roberto & Romero, Daniel (2017). Defending Surveillance Sensor Networks Against Data-Injection Attacks via Trusted Nodes . European Signal Processing Conference. ISSN 2076-1465. s. 380–384. doi: 10.23919/EUSIPCO.2017.8081233.
• 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), s. 2547–2560. doi: 10.1109/TSP.2017.2666775.
• 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), s. 856–869. doi: 10.1109/JSTSP.2017.2726976.
• 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), s. 6448–6461. doi: 10.1109/TSP.2017.2755597.

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• Cardenas, Juan Diego; Beferull-Lozano, Baltasar; Elnourani, Mohamed & Romero, Daniel (2022). RISK-AWARE PARTICLE FILTERING FOR STATE ESTIMATION IN RECIRCULATING AQUACULTURE SYSTEMS.
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2019). Online Topology Identification from Vector Autoregressive Time Series. Vis sammendrag

  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.
• Elnourani, Mohamed; Hamid, Mohamed; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Underlay Device-to-Device Communications on Multiple Channels.
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2017). Online Topology Estimation for Vector Autoregressive Processes in Data Networks.
• Teganya, Yves; Romero, Daniel & Beferull-Lozano, Baltasar (2020). Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels. Universitetet i Agder. ISSN 978-82-7117-999-1. Fulltekst i vitenarkiv
• 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 978-82-8427-000-5. Fulltekst i vitenarkiv
• Elnourani, Mohamed; Beferull-Lozano, Baltasar & Romero, Daniel (2020). Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications. 07 Media. ISSN 978-82-7117-995-3. Fulltekst i vitenarkiv

Se alle arbeider i Cristin