Baltasar Enrique Beferull Lozano

• Nama, Ajay Nagendra; Saad, Leila Ben; Beferull-Lozano, Baltasar & Zhou, Jing (2023). Neighborhood Graph Filters Based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning. In IEEE, . (Eds.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-3182-0. doi: 10.1109/IECON51785.2023.10311938. Show summary

  Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-agent systems cooperate to achieve a common goal, has been shown useful in many applications such as collaborative robots, autonomous driving or video games involving teams. In this paper, we propose two multi-agent deep reinforcement learning (MADRL) frameworks for value function factorization built by using Graph Convolutional Neural Networks (GCNN) based on neighborhood graph filters (NGFs). These MADRL frameworks are based on the paradigm of centralized training with decentralized execution (CTDE). In this work, we show that the superior stability of the NGFs as compared to standard graph filters leads also to superior performance for the MADRL algorithms. In the first MADRL framework, the NGF-based GCNN is used to predict the local q-value at each of the agents, while in the other one, the NGF-based GCNN is used to mix the local q-values to generate a global Q-value. We have compared the performance of NGF-based GCNNs over state-of-the-art graph neural networks for value function factorization in the MADRL framework for the StarCraft II and Coalition Structure Generation problems. The results show that the proposed MARL frameworks outperform the existing state-of-art architectures.

• Zaman, Bakht; Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2023). Online Joint Topology Identification and Signal Estimation From Streams With Missing Data. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-7778. 9, p. 691–704. doi: 10.1109/TSIPN.2023.3324569. Show summary

  Identifying the topology underlying a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model-based topologies capture dependencies among time series and are often inferred from observed spatio-temporal data. When data are affected by noise and/or missing samples, topology identification and signal recovery (reconstruction) tasks must be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. This study proposes an online algorithm to overcome these challenges in estimating VAR model-based topologies, having constant complexity per iteration, which makes it interesting for big-data scenarios. The inexact proximal online gradient descent framework is used to derive a performance guarantee for the proposed algorithm, in the form of a dynamic regret bound. Numerical tests are also presented, showing the ability of the proposed algorithm to track time-varying topologies with missing data in an online fashion.

• Moreno, Emilio Ruiz & Beferull-Lozano, Baltasar (2023). An Online Multiple Kernel Parallelizable Learning Scheme. IEEE Signal Processing Letters. ISSN 1070-9908. 31, p. 121–125. doi: 10.1109/LSP.2023.3343185. Full text in Research Archive Show summary

  —The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.

• Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar (2023). Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Show summary

  While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.

• Moreno, Emilio Ruiz; Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2023). A Trainable Approach to Zero-Delay Smoothing Spline Interpolation. IEEE Transactions on Signal Processing. ISSN 1053-587X. 71, p. 4317–4329. doi: 10.1109/TSP.2023.3329946. Full text in Research Archive
• Krishnan, Joshin Parakkulangarayil; Money, Rohan Thekkemarickal; Beferull-Lozano, Baltasar & Isufi, Elvin (2023). Simplicial Vector Autoregressive Model For Streaming Edge Flows, 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE conference proceedings. ISSN 978-1-7281-6327-7. doi: 10.1109/ICASSP49357.2023.10096095. Show summary

  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.

• Jaiswal, Rahul Kumar; Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2023). Location-free Indoor Radio Map Estimation using Transfer learning, 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). IEEE conference proceedings. ISSN 979-8-3503-1114-3. doi: 10.1109/VTC2023-Spring57618.2023.10200979. Full text in Research Archive Show summary

  Accurate estimation of radio maps is important for various applications of wireless communications, such as network planning, and resource allocation. To learn accurate radio map models, one needs to have accurate knowledge of transmitter and receiver locations. However, it is difficult to obtain accurate locations in practice, especially, in scenarios having a high degree of wireless multi-path. Alternatively, time of arrival (ToA) features, which are easier to obtain, can be employed for estimating radio maps. To this end, this paper investigates the application of transfer learning method using ToA features for estimating radio maps under indoor wireless communications. The performance is compared with the scenarios where only the locations of receivers and both ToAs and locations of receivers, are used for estimating radio maps, assuming that locations are known. Due to the changes in propagation characteristics, a radio map model learned in a specific wireless environment cannot be directly employed in a new wireless environment. To address this issue, a data-driven transfer learning method is designed that transfers and fine-tunes a deep neural network model learned for a radio map from a source wireless environment to other distinct (target) wireless environments. Our proposed method predicts the training data required in the new wireless environments using a data-driven similarity measure. Our results demonstrate that using ToA (location-free) features results in a superior performance for estimating radio maps in terms of the necessary number of sensor measurements for estimating radio maps with a good accuracy, as compared to a location-based approach, where it may be difficult to have accurate location estimations. It leads to a saving of 70-90% of the necessary sensor measurement data for a mean square error (MSE) of 0.004.

• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar & Isufi, Elvin (2023). Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models. IEEE Open Journal of Signal Processing. ISSN 2644-1322. 4, p. 61–70. doi: 10.1109/OJSP.2023.3241580. Full text in Research Archive Show summary

  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. 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.

• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2023). Sparse Online Learning With Kernels Using Random Features for Estimating Nonlinear Dynamic Graphs. IEEE Transactions on Signal Processing. ISSN 1053-587X. 71, p. 2027–2042. doi: 10.1109/TSP.2023.3282068. Full text in Research Archive Show summary

  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.

• Ghari, Siavash Mollaebrahim & Beferull-Lozano, Baltasar (2023). Distributed Linear Network Operators Based on Successive Graph Shift Matrices. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-7778. 9, p. 315–328. doi: 10.1109/TSIPN.2023.3271148. Show summary

  In the context of graph signal processing, the existing distributed approaches for implementing linear network operators rely on the notion of graph shift matrix, which captures the local structure of the graph. Most of the existing approaches consider only a restricted set of linear network operators. However, in this paper, we focus on approximating general linear network operators as fast as possible after a finite number of local exchanges, with a negligible error. We propose a new distributed successive method based on designing a sequence of different graph shift matrices, which are optimized to approximate the desired network operator in an approximately minimal number of iterations. We also consider the robustness of the distributed computation of linear operators against graph perturbations. For this, we first analyze the effect of graph perturbations on our successive method and then, we incorporate the effect of graph perturbations in our design by proposing an online kernel-based estimator, which enables the nodes of the network to estimate the missing values caused by graph perturbations across iterations via available information received from neighbor nodes. Our numerical results demonstrate the superior performance of our methods over the existing state-of-the-art approaches.

• Singh, Aditya; Redhu, Surender; Beferull-Lozano, Baltasar & Hegde, Rajesh M. (2023). Network-aware RF-energy harvesting for designing energy efficient IoT networks. Internet of Things: Engineering Cyber Physical Human Systems. ISSN 2542-6605. 22. doi: 10.1016/j.iot.2023.100770. Show summary

  Energy Harvesting plays a crucial role in improving the network lifetime of an IoT application. Radio Frequency (RF) energy is one of the most prominent ambient energy sources available that can be used for harvesting energy. Moreover, a number of dedicated RF-energy transmitters is required for sufficient energy transfer to the power deficit IoT nodes. However, the appropriate positioning of these RF-energy transmitters in the network is a challenging issue. In this context, a network-aware RF-energy transmitter positioning scheme is proposed in this work. This RFenergy transmitter deployment scheme considers different network performance parameters such as energy-hole information, node-connectivity information, and data routing information for RF-energy transmitter placement. Apart from that, the amount of RF-energy harvested is constrained to the RF-energy carried by the propagating RF-waves. Moreover, the wireless medium uncertainties result in severe attenuation of the propagating RF-waves. In this regard, a robust hybrid RF-wave propagation model is also proposed. The proposed model amalgamates different wave propagation mechanisms for signal strength prediction in the propagating RF-waves. This helps in improving the modelling accuracy of the RF-waves. The proposed model also predicts the amount of residual RF-energy available in the propagating RF-wave for harvesting. This model is also validated against the practical datasets generated from experiments. The proposed transmitter positioning scheme, along with the hybrid RF-wave propagation model, overcome the energy hole issue in the IoT networks and improves energy efficiency. Extensive simulations are conducted under various network scenarios to evaluate the energy harvesting performance of the proposed methods. It is observed that the proposed network-aware RF-energy harvesting scheme improves the network performance in terms of energy harvesting, network coverage, and the lifetime of IoT network.

• Roy, Kevin; Lopez Ramos, Luis Miguel & Beferull-Lozano, Baltasar (2022). Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries, 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE conference proceedings. ISSN 978-1-6654-5906-8. doi: 10.1109/IEEECONF56349.2022.10051968.
• 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. p. 829–833. doi: 10.1109/IEEECONF56349.2022.10051993. 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.

• Kumar, Sarang; Elnourani, Mohamed; Beferull-Lozano, Baltasar & Redhu, Surender (2022). A Deep Learning-Based Channel Aware Single Step Signal Detection in Downlink Multi-User NOMA, 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE conference proceedings. ISSN 978-1-6654-5468-1. doi: 10.1109/VTC2022-Fall57202.2022.10013001.
• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2022). Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs. In N, N (Eds.), 30th European Signal Processing Conference (EUSIPCO 2022). European Association for Signal Processing. ISSN 978-90-827970-9-1. p. 687–691. Full text in Research Archive Show summary

  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.

• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar & Isufi, Elvin (2022). Online Edge Flow Imputation on Networks. IEEE Signal Processing Letters. ISSN 1070-9908. 30, p. 115–119. doi: 10.1109/LSP.2022.3221846. Full text in Research Archive
• Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2022). Resource Allocation for Underlay Interfering D2D Networks With Multiantenna and Imperfect CSI. IEEE Transactions on Communications. ISSN 0090-6778. 70(9), p. 6066–6082. doi: 10.1109/TCOMM.2022.3194193.
• Singh, Jayant; Zhou, Jing; Beferull-Lozano, Baltasar & Tyapin, Ilya (2022). Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9969046.
• Saad, Leila Ben; Nama, Ajay Nagendra & Beferull-Lozano, Baltasar (2022). Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors , 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/SPAWC51304.2022.9834020. Show summary

  Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.

• Cardenas, Juan Diego; Elnourani, Mohamed Gafar Ahmed & Beferull-Lozano, Baltasar (2022). Forecasting Aquaponic Systems Behaviour With Recurrent Neural Networks Models . Proceedings of the Northern Lights Deep Learning Workshop. 3. doi: 10.7557/18.6236. Full text in Research Archive Show summary

  Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the machine learning life-cycle with industrial time-series data, i.e. data acquisition, pre-processing, feature engineering, architecture selection, training, and model verification.

• Jaiswal, Rahul Kumar; Deshmukh, Siddharth; Elnourani, Mohamed & Beferull-Lozano, Baltasar (2022). Transfer Learning Based Joint Resource Allocation for Underlay D2D Communications, 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE conference proceedings. ISSN 978-1-6654-4266-4. p. 1479–1484. doi: 10.1109/WCNC51071.2022.9771636. Full text in Research Archive Show summary

  In this paper, we investigate the application of transfer learning to train a Deep Neural Network (DNN) model for joint channel and power allocation in underlay device-todevice (D2D) communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect channel state information (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.

• Jaiswal, Rahul Kumar; Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2022). Deep Transfer Learning Based Radio Map Estimation for Indoor Wireless Communications , 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/SPAWC51304.2022.9833974. Full text in Research Archive Show summary

  This paper investigates the problem of transfer learning in radio map estimation for indoor wireless communications, which can be exploited for different applications, such as channel modelling, resource allocation, network planning, and reducing the number of necessary power measurements. Due to the nature of wireless communications, a radio map model developed under a particular environment can not be directly used in a new environment because of the changes in the propagation characteristics, thus creating a new model for every environment requires in general a large amount of data and is computationally demanding. To address these issues, we design an effective novel data-driven transfer learning procedure that transfers and fine-tunes a deep neural network (DNN)-based model for a radio map learned from an original indoor wireless environment to other different indoor wireless environments. Our method allows to predict the amount of training data needed in new indoor wireless environments when performing the operation of transfer learning using our similarity measure. Our simulation results illustrate that the proposed method achieves a saving of 60-70% in sensor measurement data and is able to adapt to a new wireless environment with a small amount of additional data.

• Lopez-Ramos, Luis M.; Roy, Kevin & Beferull-Lozano, Baltasar (2022). Explainable Nonlinear Modelling of Multiple Time Series with Invertible Neural Networks. In Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yildirim Yayilgan, Sule & Bajwa, Imran Sarwar (Ed.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 978-3-031-10524-1. p. 17–30. doi: 10.1007/978-3-031-10525-8_2. Show summary

  A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modeled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the backpropagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.

• 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. 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.

• Sironja, Poorva; Redhu, Surender; Hegde, Rajesh M. & Beferull-Lozano, Baltasar (2022). Cluster-based 3D-Beam Power Management in Resource-Constrained Wireless Networks, 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE conference proceedings. ISSN 978-1-6654-2104-1. p. 590–594. doi: 10.1109/COMSNETS53615.2022.9668343. Show summary

  Abstract—Various applications of wireless 5G networks demand high throughput for improving the Quality of Service. Power management of the beams plays a key role in improving the throughput in 5G-mmWave networks. However, resource constraints make beam generation a challenging issue. In this work, cluster-based 3D beam power management schemes are proposed for improving the throughput in resource-Constrained wireless networks. Firstly, the network users are divided into a number of groups. 3D beam patterns are generated from the transmitter to maximize the throughput of users based on clustering information. The proposed cluster-based beam management scheme considers a limited amount of power resources at the transmitter. Available transmit power is distributed optimally to every cluster in the network. The performance is improved further by identifying the outliers in the clusters. The tradeoff between the transmit power and users served decides the threshold on outlier detection. The performance of proposed cluster-based 3D beam patterns is evaluated under different network scenarios using numeric simulations. The proposed method shows significant improvements in terms of throughput, network coverage, average signal-to-noise ratio (SNR), and transmit power consumption. The performance of the proposed methods is also compared with the alternative schemes under power-constrained scenarios.

• Sharan, Ravi; Deshmukh, Siddharth; Pillai, Sibi Raj B. & Beferull-Lozano, Baltasar (2021). Energy Efficient AoI Minimization in Opportunistic NOMA/OMA Broadcast Wireless Networks. IEEE Transactions on Green Communications and Networking (ITGCN). 6(2), p. 1009–1022. doi: 10.1109/TGCN.2021.3135351.
• Saad, Leila Ben; Beferull-Lozano, Baltasar & Isufi, Elvin (2021). Quantization Analysis and Robust Design for Distributed Graph Filters. IEEE Transactions on Signal Processing. ISSN 1053-587X. doi: 10.1109/TSP.2021.3139208. Full text in Research Archive Show summary

  Distributed graph filters have recently found appli- cations in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and recon- struction. 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 two most common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive anal- ysis, we devise theoretical performance guarantees on the filter performance when 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 control on the quantization noise than the fixed-stepsize quantization. For ARMA graph filters, we show that 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.

• Saad, Leila Ben & Beferull-Lozano, Baltasar (2021). Quantization in Graph Convolutional Neural Networks, 2021 29th European Signal Processing Conference (EUSIPCO). IEEE conference proceedings. ISSN 978-9-0827-9706-0. doi: 10.23919/EUSIPCO54536.2021.9615973. Full text in Research Archive
• Redhu, Surender; Singh, Amrendra P.; Hedge, Rajesh M & Beferull-Lozano, Baltasar (2021). A Cluster based Sensor-Selection Scheme for Energy-Efficient Agriculture Sensor Networks, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE conference proceedings. ISSN 978-1-7281-9794-4. doi: 10.1109/CCNC49032.2021.9369595.
• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2021). Random Feature Approximation for Online Nonlinear Graph Topology Identification. Machine Learning for Signal Processing. ISSN 1551-2541. doi: 10.1109/MLSP52302.2021.9596512.
• Moreno, Emilio Ruiz & Beferull-Lozano, Baltasar (2021). Tracking of Quantized Signals Based on Online Kernel Regression. Machine Learning for Signal Processing. ISSN 1551-2541. doi: 10.1109/MLSP52302.2021.9596115. Full text in Research Archive
• Bhardwaj, Jyotirmoy; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2021). Data-Driven Pump Scheduling for Cost Minimization in Water Networks. In NN, NN (Eds.), 2021 IEEE International Conference on Autonomous Systems (ICAS). IEEE conference proceedings. ISSN 978-1-7281-7289-7. p. 197–201. doi: 10.1109/ICAS49788.2021.9551168. Full text in Research Archive Show summary

  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.

• Bhardwaj, Jyotirmoy; Krishnan, Joshin Parakkulangarayil; Marin, Diego F. Larios; Beferull-Lozano, Baltasar; Cenkeramaddi, Linga Reddy & Harman, Christopher Peter (2021). Cyber-Physical Systems for Smart Water Networks: A Review. IEEE Sensors Journal. ISSN 1530-437X. 21(23), p. 26447–26469. doi: 10.1109/JSEN.2021.3121506. Full text in Research Archive Show summary

  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.

• Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2021). Online Non-linear Topology Identification from Graph-connected Time Series. In NN, NN (Eds.), 2021 IEEE Data Science and Learning Workshop (DSLW). IEEE conference proceedings. ISSN 978-1-6654-2825-5. doi: 10.1109/DSLW51110.2021.9523399.
• Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2021). Distributed Resource Allocation in Underlay Multicast D2D Communications. IEEE Transactions on Communications. ISSN 0090-6778. 69(5), p. 3409–3422. doi: 10.1109/TCOMM.2021.3058374.
• Ghari, Siavash Mollaebrahim & Beferull-Lozano, Baltasar (2021). Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection. IEEE Transactions on Signal Processing. ISSN 1053-587X. 69, p. 2056–2069. doi: 10.1109/TSP.2021.3066787.
• 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.
• Ghari, Siavash Mollaebrahim; Beferull-Lozano, Baltasar & Moreno, Emilio Ruiz (2020). Decentralized Subspace Projection for Asymmetric Sensor Networks. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2020-Fall49728.2020.9348681.
• Lopez Ramos, Luis Miguel; Teganya, Yves; Beferull-Lozano, Baltasar & Kim, Seung-Jun (2020). Channel Gain Cartography via Mixture of Experts. In NN, NN (Eds.), 2020 IEEE Global Communications Conference (GLOBECOM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8298-8. doi: 10.1109/GLOBECOM42002.2020.9322198. Full text in Research Archive Show summary

  In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multipath channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps consists of features extracted from the positioning signals, instead of location estimates. The location-based and the location-free approaches have complementary merits. In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed in a mixture-of-experts framework.

• Sharma, Sanjeev; Deka, Kuntal & Beferull-Lozano, Baltasar (2020). Low-complexity detection for uplink massive MIMO SCMA systems. IET Communications. ISSN 1751-8628. p. 51–59. doi: 10.1049/cmu2.12057. Full text in Research Archive
• 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. p. 220–225. doi: 10.1109/TSP.2020.3042940. Full text in Research Archive
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2020). Autonomous mooring towards autonomous maritime navigation and offshore operations, 15th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers (IEEE). ISSN 978-1-7281-5168-7. p. 1171–1175. doi: 10.1109/ICIEA48937.2020.9248169. Full text in Research Archive
• 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. Full text in Research Archive
• Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2020). Online Hyperparameter Search Interleaved with Proximal Parameter Updates, 28th European Signal Processing Conference (EUSIPCO 2020). European Signal Processing Conference. ISSN 978-9-0827-9705-3. p. 2085–2089. Full text in Research Archive Show summary

  There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical learning, since commonly applied search methods (such as grid search with N-fold cross-validation) are inefficient and/or approximate. Previously existing gradient-based HO algorithms that rely on the smoothness of the cost function cannot be applied in problems such as Lasso regression. In this contribution, we develop a HO method that relies on the structure of proximal gradient methods and does not require a smooth cost function. Such a method is applied to Leave-one-out (LOO)-validated Lasso and Group Lasso, and an online variant is proposed. Numerical experiments corroborate the convergence of the proposed methods to stationary points of the LOO validation error curve, and the improved efficiency and stability of the online algorithm

• Zaman, Bakht; Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2020). Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies. In Li, Zhengguo (Eds.), Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications(ICIEA 2020). IEEE conference proceedings. ISSN 978-1-7281-5168-7. p. 939–944. doi: 10.1109/ICIEA48937.2020.9248365. Show summary

  Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic regret of the proposed algorithm. Numerical tests show that the proposed algorithm can track changes under different models of time-varying topologies.

• Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2020). Reliable Multicast D2D Communication over Multiple Channels in Underlay Cellular Networks. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops. ISSN 2166-9570. doi: 10.1109/PIMRC48278.2020.9217293. Full text in Research Archive
• Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2020). Reliable Underlay D2D Communications over Multiple Transmit Antenna Framework, ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-5089-5. doi: 10.1109/ICC40277.2020.9149079.
• 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. Full text in Research Archive
• Ben Saad, Leila & Beferull-Lozano, Baltasar (2020). Accurate Graph Filtering in Wireless Sensor Networks. IEEE Internet of Things Journal. ISSN 2327-4662. doi: 10.1109/JIOT.2020.3010610. Full text in Research Archive
• Deka, Kuntal; Priyadarsini, Minerva; Sharma, Sanjeev & Beferull-Lozano, Baltasar (2020). Design of SCMA Codebooks using Differential Evolution. IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC). ISSN 2474-9133. doi: 10.1109/ICCWorkshops49005.2020.9145202.
• Idsoe, Henning; Cenkeramaddi, Linga Reddy; Beferull-Lozano, Baltasar & Soumya, J. (2019). Phase-noise Impact on the Performance of mmWave-radars. International Symposium on Advanced Networks and Telecommunication Systems (ANTS). ISSN 2153-1676. 2019-. doi: 10.1109/ANTS47819.2019.9117975.
• Frøytlog, Anders; Haglund, Magne Arild; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar (2019). Design and implementation of a long-range low-power wake-up radio and customized DC-MAC protocol for LoRaWAN. International Symposium on Advanced Networks and Telecommunication Systems (ANTS). ISSN 2153-1676. 2019-. doi: 10.1109/ANTS47819.2019.9118165.
• Idsøe, Henning; Cenkeramaddi, Linga Reddy; J, Soumya & Beferull-Lozano, Baltasar (2019). Phase-noise Impact on the Performance of mmWave-RADARs, 13th IEEE International Conference on Advanced Networks and Telecommunications Systems, 2019 (IEEE ANTS 2019). IEEE conference proceedings. ISSN 978-1-7281-3715-5. p. 1–6.
• Frøytlog, Anders; Haglund, Magne Arild; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar (2019). Design and implementation of a long-range low-power wake-up radio and customized DC-MAC protocol for LoRaWAN, 13th IEEE International Conference on Advanced Networks and Telecommunications Systems, 2019 (IEEE ANTS 2019). IEEE conference proceedings. ISSN 978-1-7281-3715-5. p. 1–5.
• Ben Saad, Leila; Isufi, Elvin & Beferull-Lozano, Baltasar (2019). Graph Filtering with Quantization over Random Time-varying Graphs, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE conference proceedings. ISSN 978-1-7281-2723-1. doi: 10.1109/GlobalSIP45357.2019.8969270. Show summary

  Distributed graph filters can be implemented over wireless sensor networks by means of cooperation and exchanges among nodes. However, in practice, the performance of such graph filters is deeply affected by the quantization errors that are accumulated when the messages are transmitted. The latter is paramount to overcome the limitations in terms of bandwidth and computation capabilities in sensor nodes. In addition to quantization errors, distributed graph filters are also affected by random packet losses due to interferences and background noise, leading to the degradation of the performance in terms of the filtering accuracy. In this work, we consider the problem of designing graph filters that are robust to quantized data and time-varying topologies. We propose an optimized method that minimizes the quantization error, while ensuring an accurate filtering over time-varying graph topologies. The efficiency of the proposed theoretical findings is validated by numerical results in random wireless sensor networks.

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

• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Frøytlog, Anders; Beferull-Lozano, Baltasar; Soumya, J. & Bhange, Meghana (2019). Long-range & Self-powered IoT Devices for Agriculture & Aquaponics Based on Multi-hop Topology. In N, N (Eds.), 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE conference proceedings. ISSN 978-1-5386-4980-0. doi: 10.1109/WF-IoT.2019.8767196.
• Frøytlog, Anders; Haglund, Magne Arild; Cenkeramaddi, Linga Reddy; Jordbru, Thomas; Kjellby, Rolf Arne & Beferull-Lozano, Baltasar (2019). Design and implementation of a long-range low-power wake-up radio for IoT devices. In N, N (Eds.), 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE conference proceedings. ISSN 978-1-5386-4980-0. doi: 10.1109/WF-IoT.2019.8767353.
• Ben Saad, Leila & Beferull-Lozano, Baltasar (2019). Graph Filtering of Time-Varying Signals over Asymmetric Wireless Sensor Networks, 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.2019.8815521.
• 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. 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.

• Asensio-Marco, César; Alonso-Román, Daniel & Beferull-Lozano, Baltasar (2019). Cross-layer MAC Protocol for Unbiased Average Consensus under Random Interference. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-7778. 5(2), p. 320–333. doi: 10.1109/TSIPN.2018.2873070. Full text in Research Archive Show summary

  Wireless Sensor Networks have been revealed as a powerful technology to solve many different problems through sensor nodes cooperation. One important cooperative process is the so-called average gossip algorithm, which constitutes a building block to perform many inference tasks in an efficient and distributed manner. From the theoretical designs proposed in most previous work, this algorithm requires instantaneous symmetric links in order to reach average consensus. However, in a realistic scenario wireless communications are subject to interferences and other environmental factors, which results in random instantaneous topologies that are, in general, asymmetric. Consequently, the estimation of the average obtained by the gossip algorithm is a random variable, which its realizations may significantly differ from the average value. In the present work, we first derive a sufficient conditions for any MAC protocol to guarantee that the expected value of the obtained consensus random variable is the average of the initial values (unbiased estimator), while the variance of the estimator is minimum. Then, we propose a cross-layer and distributed link scheduling protocol based on carrier sense, which besides avoiding collisions, ensures both an unbiased estimation and close to minimum variance values. Extensive numerical results are presented to show the validity and efficiency of the proposed approach.

• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 10 contributors for this article] (2018). Design, Development and Deployment of Low-cost Short-range Self-powered Wireless IoT Devices, 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). IEEE conference proceedings. ISSN 978-1-5386-9172-4. p. 104–107. doi: 10.1109/iSES.2018.00031. Show summary

  This article presents the design and prototype implementation of a low-cost and short-range self-powered wireless IoT device based on energy harvesting for both indoor and outdoor applications. Prototyped devices are deployed in a star network configuration with a custom protocol. Based on measurements, devices achieve a line-of-sight range of 228.5m above 40m from the ground level. Nodes are powered based on energy harvesting from a small 0.36W solar panel and 120mAh lithium button cell as storage elements. The test in the well-lit room shows an average harvested power of 941.94μW over a period of 2.5 days, while under the low lighting conditions showed an average of 212μW over a period of 24h. From measurements, a fully charged rechargeable 120mAh cell lasts for 278 days with 55s transmission interval. Temperature, visible lights level and relative humidity sensors are integrated into the nodes.

• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2018). Design and Prototype Implementation of Long-range Self-powered Wireless IoT Device, 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). IEEE conference proceedings. ISSN 978-1-5386-9172-4. p. 215–218. doi: 10.1109/iSES.2018.00054. Show summary

  This paper presents the design and prototype implementation of long-range self-powered wireless IoT devices using nRF52840 based on energy harvesting. The test-bed is setup in both star and multi-hop configurations with optimized custom protocols. In both network configurations, nodes consume less power than what is harvested in an indoor light environment using a small 0.36W rated monocrystalline solar panel. The average power by which the battery was charged during the test was 941.94μW in an indoor environment. Nodes are able to operate for 12 months using a fully charged 120mAh rated rechargeable coin cell battery with 55s transmission interval. Based on measurements, a line of sight range of 1.8km is obtained using coded transmissions. Sensors of temperature, relative humidity and visible light are integrated into the nodes.

• Kjellby, Rolf Arne; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Cenkeramaddi, Linga Reddy; Mohamed, Hamid & Beferull-Lozano, Baltasar (2018). Self-Powered IoT Device for Indoor and Outdoor Applications, 2018 31st International Conference on VLSI Design and 2018 17th International Conference on Embedded Systems (VLSID). IEEE conference proceedings. ISSN 978-1-5386-3692-3. p. 455–456. doi: 10.1109/VLSID.2018.110. Show summary

  This paper presents a proof of concept for self-powered Internet of Things (IoT) device, which is maintenance free and completely self-sustainable through energy harvesting. These IoT devices can be deployed in large scale and placed anywhere as long as they are in range of a gateway, and as long as there is sufficient light levels for the solar panel, such as indoor lights. A complete IoT device is designed, prototyped and tested. The IoT device can potentially last for more than 5 months (transmission interval of 30 seconds) on the coin cell battery (capacity of 120mAh) without any energy harvesting, sufficiently long for the dark seasons of the year. The sensor node contains ultra-low power sensors for temperature, humidity and light levels, with the possibility of adding several more sensors.

• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2018). Self-powered IoT Device based on Energy Harvesting for Remote Applications, 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE conference proceedings. ISSN 978-1-5386-8134-3. doi: 10.1109/ANTS.2018.8710171.
• Alonso, Daniel; Asensio-Marco, César & Beferull-Lozano, Baltasar (2018). Consensus-Based Distributed Detection and State Estimation of Biofilm in Reverse Osmosis Membranes by WSNs. In Tsakalides, Panagiotis; Panousopoulo, Athanasia; Tsagkatakis, Grigorios & Montestruque, Luis (Ed.), Smart Water Grids. Taylor & Francis. ISSN 9781351986175. p. 41–69. doi: 10.1201/b21948.
• 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. 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 conference proceedings. ISSN 978-1-7281-1295-4. doi: 10.1109/GlobalSIP.2018.8646531. Full text in Research Archive
• Kjellby, Rolf Arne; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Cenkeramaddi, Linga Reddy; Hamid, Mohamed & Beferull-Lozano, Baltasar (2018). Self-Powered IoT Device for Indoor Applications. VLSI Design. ISSN 1063-9667. 2018-January, p. 455–456. doi: 10.1109/VLSID.2018.110.
• Asensio-Marco, César & Beferull-Lozano, Baltasar (2018). Adaptive Medium Access Control for Distributed Processing in Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-7778. 5(1), p. 113–126. doi: 10.1109/TSIPN.2018.2866324. Full text in Research Archive
• Hamid, Mohamed & Beferull-Lozano, Baltasar (2018). Joint Topology and Radio Resource Optimization for Device-to-Device Based Mobile Social Networks, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE conference proceedings. ISSN 978-1-5386-4658-8. p. 3789–3793. doi: 10.1109/ICASSP.2018.8461962. Full text in Research Archive Show summary

  In this paper, we consider a joint topology and radio resource optimization for device-to-device (D2D) based mobile social networks. The considered social network is an interest based which is modeled as a d -intersection binomial random graph. The Radio network is also modeled as a random graph where an edge between any two distinct nodes is activated with a certain probability that is equivalent to the probability of exceeding a certain signal to interference ratio for that link. The entire network is then modeled as an intersection graph between the social and radio induced graphs. Thereafter, network topology is optimized such that enabled social edges satisfy certain network connectivity constrains under specific radio environment characteristics. Radio resource allocation is performed to maximize the radio resource utilization exploiting both social ties awareness among the network nodes and knowledge of channel gains among users' locations. We formulate our radio resource allocation problem as a semidefinite program over a graph representing the network topology. Simulation based numerical results are shown in terms of achieved link efficiency and optimized topology parameters.

• Asensio-Marco, César & Beferull-Lozano, Baltasar (2018). Energy Efficient Consensus Over Directed Graphs, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE conference proceedings. ISSN 978-1-5386-4658-8. p. 4124–4128. doi: 10.1109/ICASSP.2018.8461849. Full text in Research Archive Show summary

  Consensus algorithms are iterative methods that represent a basic building block to achieve superior functionalities in increasingly complex sensor networks by facilitating the implementation of many signal-processing tasks in a distributed manner. Due to the heterogeneity of the devices, which may present very different capabilities (e.g. energy supply, transmission range), the energy often becomes a scarce resource and the communications turn into directed. To maximize the network lifetime, a magnitude that in this work measures the number of consensus processes that can be executed before the first node in the network runs out of battery, we propose a topology optimization methodology for directed networks. Numerical results corroborate the merits of this work.

• Ben Saad, Leila & Beferull-Lozano, Baltasar (2018). Stochastic Graph Filtering under Asymmetric Links in Wireless Sensor Networks, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE conference proceedings. ISSN 978-1-5386-3512-4. doi: 10.1109/SPAWC.2018.8445848. Full text in Research Archive Show summary

  Wireless sensor networks (WSN s) are often characterized by random and asymmetric packet losses due to the wireless medium, leading to network topologies that can be modeled as random, time-varying and directed graphs. Most of existing works related to graph filtering in the context of WSNs assume that the probability of delivering an information from one node to a neighbor node is the same as in the reverse direction. This assumption is not realistic due to the typical link asymmetry in WSNs caused by interferences and background noise. In this work, we analyze the problem of applying stochastic graph filtering over random time-varying asymmetric network topologies. We show that it is possible to perform stochastic graph filtering under asymmetric links with node-variant graph filters, while optimizing a trade-off between the expected error (bias) and the variance of the error, with respect to performing graph filtering over a fixed static topology given by a certain connectivity radius of the nodes.

• 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. 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.

• 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. 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.

• 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. 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.

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• Jaiswal, Rahul Kumar; Elnourani, Mohamed; Deshmukh, Siddharth & Beferull-Lozano, Baltasar (2023). Location-free Indoor Radio Map Estimation using Transfer learning.
• Cardenas, Juan Diego; Beferull-Lozano, Baltasar; Elnourani, Mohamed & Romero, Daniel (2022). RISK-AWARE PARTICLE FILTERING FOR STATE ESTIMATION IN RECIRCULATING AQUACULTURE SYSTEMS.
• Ghari, Siavash Mollaebrahim & Beferull-Lozano, Baltasar (2022). Erratum: Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection (IEEE Trans. Signal Process. (2021) 69 (2056-2069) DOI: 10.1109/TSP.2021.3066787). IEEE Transactions on Signal Processing. ISSN 1053-587X. 70, p. 2842–2843. doi: 10.1109/TSP.2022.3176492.
• Lopez-Ramos, Luis M. & Beferull-Lozano, Baltasar (2021). Online Hyperparameter Search Interleaved with Proximal Parameter Updates.
• Liltved, Helge & Beferull-Lozano, Baltasar (2020). Hydroponic systems for crop growth.
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2020). Autonomous mooring towards autonomous maritime navigation and offshore operations.
• Frøytlog, Anders; Haglund, Magne Arild; Cenkeramaddi, Linga Reddy; Jordbru, Thomas; Kjellby, Rolf Arne & Beferull-Lozano, Baltasar (2019). Design and implementation of a long-range low-power wake-up radio for IoT devices.
• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Frøytlog, Anders; Beferull-Lozano, Baltasar; J, Soumya & Bhange, Meghana (2019). Long-range & Self-powered IoT Devices for Agriculture & Aquaponics Based on Multi-hop Topology.
• Frøytlog, Anders; Haglund, Magne Arild; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar (2019). Design and implementation of a long-range low-power wake-up radio and customized DC-MAC protocol for LoRaWAN.
• Idsøe, Henning; Cenkeramaddi, Linga Reddy; J, Soumya & Beferull-Lozano, Baltasar (2019). Phase-noise Impact on the Performance of mmWave-RADARs.
• Ben Saad, Leila; Isufi, Elvin & Beferull-Lozano, Baltasar (2019). Graph Filtering with Quantization over Random Time-varying Graphs.
• Zaman, Bakht; Lopez-Ramos, Luis M.; Romero, Daniel & Beferull-Lozano, Baltasar (2019). Online Topology Identification from Vector Autoregressive Time Series. 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.

• Ben Saad, Leila & Beferull-Lozano, Baltasar (2019). Graph Filtering of Time-Varying Signals over Random Asymmetric Wireless Sensor Networks.
• Beferull-Lozano, Baltasar (2018). Data Science in O&G meets Graph Signal Processing.
• Lopez-Ramos, Luis M.; Romero, Daniel; Zaman, Bakht & Beferull-Lozano, Baltasar (2018). Dynamic network identification from non-stationary vector autoregressive time series.
• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 10 contributors for this article] (2018). Design, Development and Deployment of Low-cost Short-range Self-powered Wireless IoT Devices.
• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2018). Design and Prototype Implementation of Long-range Self-powered Wireless IoT Device.
• Kjellby, Rolf Arne; Cenkeramaddi, Linga Reddy; Johnsrud, Thor Eirik; Løtveit, Svein Erik; Jevne, Geir & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2018). Self-Powered IoT Device based on Energy Harvesting for Remote Applications.
• Elnourani, Mohamed; Hamid, Mohamed; Romero, Daniel & Beferull-Lozano, Baltasar (2018). Underlay Device-to-Device Communications on Multiple Channels.
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Elnourani, Mohamed & Beferull-Lozano, Baltasar (2020). Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications. Universitetet i Agder. ISSN 978-82-7117-995-3.
• 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. Full text in Research Archive

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