Signal and Information Processing for Intelligent Systems
Our target is to deliver high-quality research to provide innovative solutions by creating theories and algorithms that blend different disciplines. We focus on establishing synergies between fundamental theory, algorithmic solutions, and application-specific implementations with the goal of providing technical contributions that have important societal impacts. By providing a healthy and vibrating working environment, SIGIPRO gives profound importance to attract, consolidate, and encourage internationally recognized human resources devoted to fundamental and applied research.
SIGIPRO aims at generating impact in multiple application domains:
- smart water networks
- energy and communication networks
- e-health
- complex finance engineering systems
- brain networks and cognitive neuroscience
- collaborative robots
- industry 4.0/5.0, digitalization of industry
- embedded intelligence for next-generation batteries
- autonomous self-navigation
- human-machine interfaces
- process optimization in wind farms and hydropower plants
- transportation and biological networks
People at Signal and Information Processing for Intelligent Systems
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Publications at Signal and Information Processing for Intelligent Systems
Journal Article
Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models
IEEE Open Journal of Signal Processing (2022).Status: Accepted
Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models
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.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Open Journal of Signal Processing |
| Publisher | IEEE |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
| DOI | 10.36227/techrxiv.21407952.v1 |
Zero-delay consistent and smooth trainable Interpolation
IEEE Transactions on Neural Networks and Learning Systems (2022).Status: Submitted
Zero-delay consistent and smooth trainable Interpolation
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Publisher | IEEE Transactions on Neural Networks and Learning Systems |
| Notes | 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. |
Online Edge Flow Imputation on Networks
IEEE Signal Processing Letters (2022).Status: Published
Online Edge Flow Imputation on Networks
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.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Signal Processing Letters |
| Publisher | IEEE |
| Notes | 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. |
| DOI | 10.1109/LSP.2022.3221846 |
Resource Allocation for Underlay Interfering D2D Networks with Multi-antenna and Imperfect CSI
IEEE Transactions on Communications 70, no. 9 (2022): 6066-6082.Status: Published
Resource Allocation for Underlay Interfering D2D Networks with Multi-antenna and Imperfect CSI
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.
| Afilliation | Communication Systems |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Communications |
| Volume | 70 |
| Issue | 9 |
| Pagination | 6066-6082 |
| Date Published | 09/2022 |
| Publisher | IEEE |
| ISSN | 0090-6778 |
| Notes | 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. |
| URL | https://ieeexplore.ieee.org/document/9841599 |
| DOI | 10.1109/TCOMM.2022.3194193 |
Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels
IEEE Transactions on Vehicular Technology 73 (2022): 3075-3091.Status: Published
Robust Sum-Rate Maximization for Underlay Device-to-Device Communications on Multiple Channels
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.
| Afilliation | Communication Systems |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Vehicular Technology |
| Volume | 73 |
| Number | 3 |
| Pagination | 3075-3091 |
| Publisher | IEEE |
| ISSN | 0018-9545 |
| Notes | 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. |
| DOI | 10.1109/TVT.2022.3145011 |
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
IEEE Transactions on Signal Processing (2022).Status: Submitted
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
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. We provide theoretical guarantees for the proposed algorithm and prove that the algorithm can achieve sublinear dynamic regret under certain reasonable assumptions. The experiments on real and synthetic data show that the proposed method outperforms its competitors.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE |
| Notes | 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. |
| DOI | 10.36227/techrxiv.19210092.v3 |
Proceedings, refereed
Simplicial Vector Autoregressive Model for Streaming Edge Flows
In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023). IEEE, 2022.Status: Accepted
Simplicial Vector Autoregressive Model for Streaming Edge Flows
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023) |
| Publisher | IEEE |
Joint Learning of Topology and Invertible Nonlinearities from Multiple Time Series
In IEEE International Conference on Machine Learning, Optimization, and Data Science (ISMODE), 2022.Status: Accepted
Joint Learning of Topology and Invertible Nonlinearities from Multiple Time Series
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | IEEE International Conference on Machine Learning, Optimization, and Data Science (ISMODE) |
| Notes | 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. |
Risk-Aware Particle-Filtering for State Estimation in Recirculating Aquaculture Systems
In IEEE Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022.Status: Published
Risk-Aware Particle-Filtering for State Estimation in Recirculating Aquaculture Systems
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | IEEE Asilomar Conference on Signals, Systems, and Computers |
| Publisher | IEEE |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries
In IEEE Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022.Status: Published
Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | IEEE Asilomar Conference on Signals, Systems, and Computers |
| Publisher | IEEE |
| Notes | 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. |