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Papers by Luis Miguel López Ramos
In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair... more 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 multi-path 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 pro...
Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic ... more Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an overlay cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering the primary users. The joint design is addressed using dynamic programming and nonlinear optimization techniques. A two-step strategy that first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy is implemented. The two-step strategy is optimal, gives rise to intuitive optimal policies, and e...
Smart distribution grids should optimally integrate stochastic renewable resources while effectin... more Smart distribution grids should optimally integrate stochastic renewable resources while effecting voltage regulation. Since some decisions have to be designed in advance, energy management is a multistage problem. For early stages, finding the optimal energy procurement accounting for the variability during real-time operation is a challenging task. The joint dispatch of slowand fast-timescale controls in a distribution grid is considered here. The substation voltage, the energy exchanged with a main grid, and the generation schedules for small diesel generators have to be decided on a slow timescale; whereas optimal photovoltaic inverter setpoints are found on a more frequent basis. While inverter and looser voltage regulation limits are imposed at all times, tighter bus voltage constraints are enforced on the average or in probability, thus enabling more efficient renewable integration. Upon reformulating the two-stage grid dispatch as a stochastic convex-concave problem, two dis...
There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical l... more 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.
In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair... more 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 prop...
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Spectrum cartography constructs maps of metrics such as channel gain or received signal power acr... more 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.
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Learning the dynamics of complex systems features a large number of applications in data science.... more Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individual edges. It contrasts with existing works, which assume that changes at all nodes are aligned in time. Numerical experiments validate the proposed schemes.
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
An important problem in data sciences pertains to inferring causal interactions among a collectio... more An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.
IEEE Transactions on Smart Grid
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks, 2012
Cognitive radios implement adaptive resource allocation schemes that exploit knowledge of the cha... more Cognitive radios implement adaptive resource allocation schemes that exploit knowledge of the channel state information to optimize the performance of the secondary users while limiting the interference to the primary users. The algorithms in this paper are designed to maximize the weighted sum-rate of secondary users which transmit orthogonally and adhere to three different constraints: i) limits on the long-term (average) power at each secondary transmitter; ii) limits on the long-term interfering power at each primary receiver; and iii) limits on the long-term capacity loss inflicted to each primary receiver. Although the long-term capacity constraints render the resultant optimization problem non-convex, it holds that it has zero-duality gap and that, due to the favorable structure in the dual domain, it can be efficiently solved.
Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks, 2012
Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic ... more Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an overlay cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering the primary users. The joint design is addressed using dynamic programming and nonlinear optimization techniques. A two-step strategy that first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy is implemented. The two-step strategy is optimal, gives rise to intuitive optimal policies, and entails a computational complexity much lower than that required to solve the original formulation. Index Terms Cognitive radios, sequential decision making, dual decomposition, partially observable Markov decision processes I. INTRODUCTION Cognitive radios (CRs) are viewed as the next-generation solution to alleviate the perceived spectrum scarcity. When CRs are deployed, the secondary users (SUs) have to sense their radio environment to optimize their communication performance while avoiding (limiting) the interference to the primary users (PUs). As a result, effective operation of CRs requires the implementation of two critical tasks: i) sensing the spectrum and ii) dynamic adaptation of the available resources according to the sensed information [10]. To carry out the sensing task two important challenges
2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2013
2010 IEEE Globecom Workshops, 2010
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011
In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair... more 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 multi-path 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 pro...
Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic ... more Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an overlay cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering the primary users. The joint design is addressed using dynamic programming and nonlinear optimization techniques. A two-step strategy that first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy is implemented. The two-step strategy is optimal, gives rise to intuitive optimal policies, and e...
Smart distribution grids should optimally integrate stochastic renewable resources while effectin... more Smart distribution grids should optimally integrate stochastic renewable resources while effecting voltage regulation. Since some decisions have to be designed in advance, energy management is a multistage problem. For early stages, finding the optimal energy procurement accounting for the variability during real-time operation is a challenging task. The joint dispatch of slowand fast-timescale controls in a distribution grid is considered here. The substation voltage, the energy exchanged with a main grid, and the generation schedules for small diesel generators have to be decided on a slow timescale; whereas optimal photovoltaic inverter setpoints are found on a more frequent basis. While inverter and looser voltage regulation limits are imposed at all times, tighter bus voltage constraints are enforced on the average or in probability, thus enabling more efficient renewable integration. Upon reformulating the two-stage grid dispatch as a stochastic convex-concave problem, two dis...
There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical l... more 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.
In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair... more 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 prop...
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Spectrum cartography constructs maps of metrics such as channel gain or received signal power acr... more 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.
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Learning the dynamics of complex systems features a large number of applications in data science.... more Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individual edges. It contrasts with existing works, which assume that changes at all nodes are aligned in time. Numerical experiments validate the proposed schemes.
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
An important problem in data sciences pertains to inferring causal interactions among a collectio... more An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.
IEEE Transactions on Smart Grid
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks, 2012
Cognitive radios implement adaptive resource allocation schemes that exploit knowledge of the cha... more Cognitive radios implement adaptive resource allocation schemes that exploit knowledge of the channel state information to optimize the performance of the secondary users while limiting the interference to the primary users. The algorithms in this paper are designed to maximize the weighted sum-rate of secondary users which transmit orthogonally and adhere to three different constraints: i) limits on the long-term (average) power at each secondary transmitter; ii) limits on the long-term interfering power at each primary receiver; and iii) limits on the long-term capacity loss inflicted to each primary receiver. Although the long-term capacity constraints render the resultant optimization problem non-convex, it holds that it has zero-duality gap and that, due to the favorable structure in the dual domain, it can be efficiently solved.
Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks, 2012
Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic ... more Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an overlay cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering the primary users. The joint design is addressed using dynamic programming and nonlinear optimization techniques. A two-step strategy that first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy is implemented. The two-step strategy is optimal, gives rise to intuitive optimal policies, and entails a computational complexity much lower than that required to solve the original formulation. Index Terms Cognitive radios, sequential decision making, dual decomposition, partially observable Markov decision processes I. INTRODUCTION Cognitive radios (CRs) are viewed as the next-generation solution to alleviate the perceived spectrum scarcity. When CRs are deployed, the secondary users (SUs) have to sense their radio environment to optimize their communication performance while avoiding (limiting) the interference to the primary users (PUs). As a result, effective operation of CRs requires the implementation of two critical tasks: i) sensing the spectrum and ii) dynamic adaptation of the available resources according to the sensed information [10]. To carry out the sensing task two important challenges
2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2013
2010 IEEE Globecom Workshops, 2010
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011