@article {43181,
	title = {Distributed Linear Network Operators via Successive Graph Shift Matrices},
	journal = {IEEE Transactions on Signal and Information Processing over Networks},
	volume = {9},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of\&nbsp;Agder. This work was supported by the PETROMAKS Smart-Rig grant 244205 and the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	month = {04/2023},
	pages = {315-328},
	publisher = {IEEE Transactions on Signal and Information Processing over Networks},
	issn = {2373-776X},
	doi = {10.1109/TSIPN.2023.3271148},
	author = {Mollaebrahim, Siavash and Beferull-Lozano, Baltasar}
}
@article {43182,
	title = {Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning},
	journal = { IEEE Internet of Things Journal},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70\&nbsp; from the Research Council of Norway.},
	publisher = {IEEE Internet of Things Journal},
	author = {Bhardwaj, Jyotirmoy and Beferull-Lozano, Baltasar and Liltved, Helge}
}
@inproceedings {43177,
	title = {Location-free Indoor Radio Map Estimation using Transfer learning},
	journal = {IEEE Vehicular Technology Conference},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of\&nbsp;Agder. This work was supported by the FRIPRO TOPPFORSK Grant WISECART 250910/F20 from the Research Council of Norway.},
	month = {2023},
	pages = {1-7},
	publisher = {IEEE},
	address = {Florence},
	doi = {10.1109/VTC2023-Spring57618.2023.10200979},
	author = {Jaiswal, Rahul and Elnourani, Mohamed and Deshmukh, Siddarth and Beferull-Lozano, Baltasar}
}
@article {43180,
	title = {Network-Aware RF-Energy Harvesting for Designing Energy Efficient IoT Networks},
	journal = {Elsevier Internet of Things},
	volume = {22},
	year = {2023},
	month = {07/2023},
	publisher = { Elsevier Internet of Things},
	issn = {2542-6605},
	doi = {https://doi.org/10.1016/j.iot.2023.100770},
	author = {Singh, Aditya and Redhu, Surrender and Beferull-Lozano, Baltasar and Rajesh M. {Hegde}}
}
@article {43179,
	title = {Online Joint Topology Identification and Signal Estimation from Streams with Missing Data},
	journal = {IEEE Transactions on Signal and Information Processing over Networks},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of\&nbsp;Agder. This work was supported by the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE Transactions on Signal and Information Processing over Networks},
	author = {Zaman, Bakht and Luis M. {Lopez} and Beferull-Lozano, Baltasar}
}
@inproceedings {42847,
	title = {Simplicial Vector Autoregressive Model for Streaming Edge Flows},
	journal = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)},
	year = {2023},
	note = {Matlab Codes:\&nbsp;https://github.com/Joshinpk/Simplicial_VAR_Model},
	month = {05/2023},
	publisher = {IEEE},
	abstract = {Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network structures such as edges, triangles, etc. The proposed model shares parameters across the simplicial signals by leveraging the simplicial convolutional filter and captures structure-aware spatio-temporal dependencies of the time-varying processes. Targetting the streaming signals from the real-world nonstationary networks, we develop a group-lasso-based online strategy to learn the proposed model. Using traffic and water distribution networks, we demonstrate that the proposed model achieves competitive signal prediction accuracy with a significantly less number of parameters than the VAR models.ICASSP 2023 has identified this paper as part of the exclusive group, ranking it within the top 3\% of all accepted papers.},
	isbn = {978-1-7281-6328-4},
	doi = {10.1109/ICASSP49357.2023.10096095},
	url = {https://ieeexplore.ieee.org/document/10096095},
	author = {Krishnan, Joshin and Money, Rohan and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@article {28392,
	title = {Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs},
	journal = {IEEE Transactions on Signal Processing},
	volume = {71},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.\&nbsp;},
	month = {06/2023},
	pages = {2027-2042},
	publisher = {IEEE},
	abstract = {Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.},
	issn = {1941-0476},
	doi = {10.1109/TSP.2023.3282068},
	url = {https://ieeexplore.ieee.org/document/10141675},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {42760,
	title = {A Trainable Approach to Zero-delay Smoothing Spline Interpolation},
	journal = {IEEE Transactions on Signal Processing},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE Transactions on Signal Processing},
	url = {ArXiv: https://arxiv.org/pdf/2203.03776.pdf},
	author = {Ruiz, Emilio and Luis Miguel {Lopez-Ramos} and Beferull-Lozano, Baltasar}
}
@article {43178,
	title = {Zero-delay Consistent Signal Reconstruction from Streamed Multi-variate time-series},
	journal = {IEEE Transactions on Signal Processing},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE Transactions on Signal Processing},
	url = {ArXiv: https://arxiv.org/pdf/2308.12459.pdf },
	author = {Ruiz, Emilio and Luis M. {Lopez} and Beferull-Lozano, Baltasar}
}
@inproceedings {42574,
	title = {Deep Transfer Learning Based Radio Map Estimation for Indoor Wireless Communications},
	journal = {IEEE International Conference on Signal Processing Advances in Wireless Communications (SPAWC)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK WISECART grant 250910/F20 from the Research Council of Norway.},
	publisher = { IEEE},
	author = {Rahul K. {Jaiswal} and Elnourani, Mohamed and Deshmukh, Siddharth and Beferull-Lozano, Baltasar}
}
@inproceedings {42758,
	title = {Joint Learning of Topology and Invertible Nonlinearities from Multiple Time Series},
	journal = {IEEE International Conference on Machine Learning, Optimization, and Data Science  (ISMODE)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	author = {Roy, Kevin and Luis Miguel {Lopez-Ramos} and Beferull-Lozano, Baltasar}
}
@inproceedings {42584,
	title = {Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries},
	journal = {IEEE Asilomar Conference on Signals, Systems, and Computers},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = { IEEE},
	author = {Roy, Kevin and Luis Miguel {Lopez-Ramos} and Beferull-Lozano, Baltasar}
}
@inproceedings {42573,
	title = {Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors},
	journal = {IEEE International Conference on Signal Processing Advances in Wireless Communications (SPAWC)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by\&nbsp;the IKTPLUSS DEEPCOBOT grant 306640/O70 from the Research Council of Norway.\&nbsp;},
	publisher = { IEEE},
	doi = {10.1109/SPAWC51304.2022.9834020},
	author = {Ben Saad, Leila and Ajay Nagendra {Nama} and Beferull-Lozano, Baltasar}
}
@article {42575,
	title = {Online Edge Flow Imputation on Networks},
	journal = {IEEE Signal Processing Letters},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real-world networks, we propose a bi-level optimization scheme that includes (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks.},
	doi = {10.1109/LSP.2022.3221846},
	url = {https://ieeexplore.ieee.org/document/9947283},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@inproceedings {42572,
	title = {Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs},
	journal = {30th European Signal Processing Conference (EUSIPCO)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = { IEEE},
	abstract = {Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging problem in real-world sensor networks, especially when the available time series are partially observed due to faulty communication links or sensor failures. The problem becomes even more challenging when the sensor dependencies are nonlinear and nonstationary. This paper proposes a kernel-based online framework using random feature approximation to jointly estimate nonlinear causal dependencies and missing data from partial observations of streaming graph-connected time series. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group lasso-based optimization framework for topology identification, which is solved online using alternating minimization techniques. The ability of the algorithm is illustrated using several numerical experiments conducted using both synthetic and real data.},
	doi = {10.23919/EUSIPCO55093.2022.9909681},
	url = {https://ieeexplore.ieee.org/document/9909681},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {42562,
	title = {Resource Allocation for Underlay Interfering D2D Networks with Multi-antenna and Imperfect CSI},
	journal = {IEEE Transactions on Communications},
	volume = {70},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK Grant WISECART 250910/F20 from the Research Council of Norway.},
	month = {09/2022},
	pages = {6066-6082},
	publisher = {IEEE},
	abstract = {Underlay Device-to-Device (D2D) communications improve the spectral efficiency by simultaneously allowing direct communication between D2D-users on the same channels as cellular users (CU). However, most related works consider perfect Channel State Information (CSI) with single-antenna transmissions and usually assign each channel to one D2D pair. In this work, we formulate an optimization problem for maximizing the aggregate rate of all D2D pairs and CUs in single and multiple antenna configurations under imperfect CSI, by optimizing channel and power resources. Our formulation guarantees probability of outage below a specified threshold and fairness in channel allocation across D2D pairs. The resulting problem is a stochastic-mixed-integer-non-convex problem, we solve it approximately by alternating between power-allocation and channel-assignment sub-problems. The stochastic objective and outage constraints are addressed by the concept of order-of-statistics in the single-antenna case and the Bernstein-type inequality in the multiple-antenna configuration. The power-allocation sub-problem is solved by exploiting a quadratic-transformation, while the channel-assignment sub-problem is solved by integer relaxation. Furthermore, two computationally efficient algorithms are proposed to approximately solve the problem in a partially decentralized manner. We also establish convergence guarantees for the different algorithms proposed in this work. Simulation results show that the proposed approach achieves higher throughput compared to the state-of-the-art alternatives.},
	issn = {0090-6778},
	doi = {10.1109/TCOMM.2022.3194193},
	url = {https://ieeexplore.ieee.org/document/9841599},
	author = {Elnourani, Mohamed and Deskmukh, Siddharth and Beferull-Lozano, Baltasar}
}
@inproceedings {42585,
	title = {Risk-Aware Particle-Filtering for State Estimation in Recirculating Aquaculture Systems},
	journal = {IEEE Asilomar Conference on Signals, Systems, and Computers},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by\&nbsp; the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	publisher = { IEEE},
	author = {Juan Diego {Cardenas-Cartagena} and Elnourani, Mohamed and Beferull-Lozano, Baltasar and Romero, Daniel}
}
@article {28424,
	title = {Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {73},
	number = {3},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the FRIPRO TOPPFORSK under Grant WISECART 250910/F20 from the Research Council of Norway.},
	pages = {3075-3091},
	publisher = {IEEE},
	abstract = {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.},
	issn = {0018-9545},
	doi = {10.1109/TVT.2022.3145011},
	author = {Elnourani, Mohamed and Deshmukh, Siddharth and Beferull-Lozano, Baltasar and Romero, Daniel}
}
@article {42845,
	title = {Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models},
	journal = {IEEE Open Journal of Signal Processing},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70\&nbsp; from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. \E{The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.},
	doi = {10.1109/OJSP.2023.3241580},
	url = {https://ieeexplore.ieee.org/document/10034854},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@inproceedings {28423,
	title = {Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications},
	journal = {IEEE Wireless Communications and Networking Conference (WCNC)},
	year = {2022},
	note = {This research work was carried out at University of Agder with funding from the FRIPRO TOPPFORSK WISECART grant 250910/F20, and completed after the SIGIPRO Department was created.},
	publisher = {IEEE},
	abstract = {In this paper, we investigate the application of transfer learning to train a DNN model for joint channel and power allocation in underlay Device-to-Device communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect CSI is not computationally demanding, compared to scenarios with imperfect CSI. Thus, a transfer learning-based approach can be exploited to transfer the DNN model trained for the perfect CSI scenarios to the imperfect CSI scenarios. We also consider the issue of defining the similarity between two types of resource allocation tasks. For this, we first determine the value of outage probability for which two resource allocation tasks are same, that is, for which our numerical results illustrate the minimal need of relearning from the transferred DNN model. For other values of outage probability, there is a mismatch between the two tasks and our results illustrate a more efficient relearning of the transferred DNN model. Our results show that the learning dataset required for relearning of the transferred DNN model is significantly smaller than the required training dataset for a DNN model without transfer learning.\&nbsp;\&nbsp;},
	author = {Rahul K. {Jaiswal} and Deshmukh, Siddharth and Elnourani, Mohamed and Beferull-Lozano, Baltasar}
}
@article {28390,
	title = {Cyber-Physical Systems for Smart Water Networks: A Review},
	journal = {IEEE Sensors Journal},
	volume = {21},
	year = {2021},
	note = {This research work was carried out at University of Agder with funding from the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway, and completed after the SIGIPRO Department was created.},
	month = {Jan-12-2021},
	pages = {26447 - 26469},
	publisher = { IEEE},
	abstract = {There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.},
	issn = {1530-437X},
	doi = {10.1109/JSEN.2021.3121506},
	url = {https://ieeexplore.ieee.org/document/9580916},
	author = {Bhardwaj, Jyotirmoy and Krishnan, Joshin and Diego F. Larios {Marin} and Beferull-Lozano, Baltasar and Linga Reddy {Cenkeramaddi} and Harman, Christopher}
}
@inproceedings {28389,
	title = {Data-Driven Pump Scheduling for Cost Minimization in Water Networks},
	journal = {2021 IEEE International Conference on Autonomous Systems (ICAS)2021 IEEE International Conference on Autonomous Systems (ICAS)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	month = {08/2021},
	publisher = {IEEE},
	address = {Montreal, QC, Canada},
	abstract = {Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural networks (DNN). We evaluate the performance of our trained DNN relative to a state-of-the-art MIO solver, and conclude that our DNN based approach can be used to minimize the pump switching and cost incurred due to dynamic energy in a given WDN with much lower complexity.},
	doi = {10.1109/ICAS49788.2021.9551168},
	url = {https://ieeexplore.ieee.org/document/9551168},
	author = {Bhardwaj, Jyotirmoy and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {28421,
	title = {Energy Efficient AoI Minimization in Opportunistic NOMA/OMA Broadcast Wireless Networks},
	journal = {IEEE Transactions on Green Communications and Networking},
	year = {2021},
	note = {This research work was carried out at University of Agder and completed after the SIGIPRO Department was created. This work was supported by the Research Council of Norway through FRIPRO TOPPFORSK under Grant WISECART 250910/F20.},
	publisher = {IEEE},
	address = {IEEE Transactions on Green Communications and Networking},
	abstract = {The concept of Age of Information (AoI) minimization in wireless networks has garnered huge interest in recent times. While current literature focuses on scheduling for AoI minimization, there is also a need to efficiently utilize the underlying physical layer resources. In this paper, we consider the problem of energy-efficient scheduling for AoI minimization in an opportunistic NOMA/OMA downlink broadcast wireless network, where the user equipment operate with diverse QoS requirements. We first formulate a resource allocation problem to minimize the average AoI of the network, with energy-efficiency factored in by restricting the long term average transmit power to a predetermined threshold. A heuristic adaptation of the driftplus-penalty approach from the Lyapunov framework is then utilized to solve the original long-term mixed-integer nonlinear problem on a per time-slot basis. The single time-slot problem is further decomposed into multiple sub-problems, solving for power allocation and user scheduling separately. However, the attained power allocation sub-problems being non-convex, we propose an efficient piece-wise linear approximation to obtain a tractable solution. The scheduling sub-problem is solved optimally by using the integrality property of the linear program. Finally, we provide extensive numerical simulations to show that our proposed approach outperforms the state of the art.},
	doi = {10.1109/TGCN.2021.3135351},
	author = {Sharan, Ravi and Deshmukh, Siddharth and Sibi R. B. {Pillai} and Beferull-Lozano, Baltasar}
}
@inproceedings {28388,
	title = {Online Non-linear Topology Identification from Graph-connected Time Series},
	journal = {2021 IEEE Data Science and Learning Workshop (DSLW)2021 IEEE Data Science and Learning Workshop (DSLW)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	month = {June/2021},
	publisher = {IEEE},
	address = {Toronto, ON, Canada},
	abstract = {Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and financial engineering. Inference of such causal dependencies, often known as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.},
	doi = {10.1109/DSLW51110.2021.9523399},
	url = {https://ieeexplore.ieee.org/document/9523399},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {28413,
	title = {Quantization Analysis and Robust Design in Distributed Graph Filters},
	journal = {IEEE Transactions on Signal Processing},
	volume = {70},
	year = {2021},
	note = {This work is a joint collaboration between SimulaMet\&nbsp; and University of Agder. This work was supported in part by the TOPPFORSK WISECART under Grant 250910/F20 and in part by the IKTPLUSS INDURB under Grant 270730/O70 from the Research Council of Norway.},
	month = {12/2021},
	pages = {643 - 658},
	publisher = { IEEE},
	address = {IEEE Transactions on Signal Processing },
	abstract = {Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities.Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the twomost common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, we devise theoretical performance guarantees on the filter performancewhen the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more reduction of the quantization noise than in the fixed-stepsize quantization. For ARMAgraph filters,we showthat decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time-invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.},
	issn = {1053-587X},
	doi = {10.1109/TSP.2021.3139208},
	url = {https://ieeexplore.ieee.org/document/9665348},
	author = {Ben Saad, Leila and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@inproceedings {28391,
	title = {Random Feature Approximation for Online Nonlinear Graph Topology Identification},
	journal = {2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	month = {10/2021},
	publisher = {IEEE},
	address = {Gold Coast, Australia},
	abstract = {Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.},
	doi = {10.1109/MLSP52302.2021.9596512},
	url = {https://ieeexplore.ieee.org/document/9596512},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@inproceedings {28422,
	title = {Tracking of quantized signals based on online kernel regression},
	journal = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {Kernel-based approaches have achieved noticeable success as non-parametric regression methods under the framework of stochastic optimization. However, most of the kernel-based methods in the literature are not suitable to track sequentially streamed quantized data samples from dynamic environments. This shortcoming occurs mainly for two reasons: first, their poor versatility in tracking variables that may change unpredictably over time, primarily because of their lack of flexibility when choosing a functional cost that best suits the associated regression problem; second, their indifference to the smoothness of the underlying physical signal generating those samples. This work introduces a novel algorithm constituted by an online regression problem that accounts for these two drawbacks and a stochastic proximal method that exploits its structure. In addition, we provide tracking guarantees by analyzing the dynamic regret of our algorithm. Finally, we present some experimental results that support our theoretical analysis and show that our algorithm has a favorable performance compared to the state-of-the-art.},
	isbn = {978-1-7281-6338-3},
	doi = {10.1109/MLSP52302.2021.9596115},
	author = {Ruiz, Emilio and Beferull-Lozano, Baltasar}
}
