Articles de revista
http://hdl.handle.net/2117/1256
20151130T03:15:15Z

Optimization of radio and computational resources for energy efficiency in latencyconstrained application offloading
http://hdl.handle.net/2117/79500
Optimization of radio and computational resources for energy efficiency in latencyconstrained application offloading
Muñoz Medina, Olga; Pascual Iserte, Antonio; Vidal Manzano, José
Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the socalled femtocloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.
20151119T17:46:50Z
Muñoz Medina, Olga
Pascual Iserte, Antonio
Vidal Manzano, José
Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the socalled femtocloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.

On the performance of Hadamard ratio detectorbased spectrum sensing for cognitive radios
http://hdl.handle.net/2117/79165
On the performance of Hadamard ratio detectorbased spectrum sensing for cognitive radios
Sedighi, Saeid; Taherpour, Abbas; Sala Álvarez, José; Khattab, Tamer
We consider the problem of multiantenna spectrum sensing (SS) in cognitive radios (CRs) when the receivers are assumed to be uncalibrated across the antennas. The performance of the Hadamard Ratio Detector (HRD) is analyzed in such a scenario. Specifically, we first derive the exact distribution of the HRD statistic under the null hypothesis, which leads to an elaborate but closedform expression for the falsealarm probability. Then, we derive a simpler and tight closedform approximation for both the falsealarm and detection probabilities by using a momentbased approximation of the HRD statistical distribution under both hypotheses. Finally, the accuracy of the obtained results is verified by simulations.
20151112T15:42:12Z
Sedighi, Saeid
Taherpour, Abbas
Sala Álvarez, José
Khattab, Tamer
We consider the problem of multiantenna spectrum sensing (SS) in cognitive radios (CRs) when the receivers are assumed to be uncalibrated across the antennas. The performance of the Hadamard Ratio Detector (HRD) is analyzed in such a scenario. Specifically, we first derive the exact distribution of the HRD statistic under the null hypothesis, which leads to an elaborate but closedform expression for the falsealarm probability. Then, we derive a simpler and tight closedform approximation for both the falsealarm and detection probabilities by using a momentbased approximation of the HRD statistical distribution under both hypotheses. Finally, the accuracy of the obtained results is verified by simulations.

Nonuniform sampling walls in wideband signal detection
http://hdl.handle.net/2117/26802
Nonuniform sampling walls in wideband signal detection
Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
This work shows the existence of sampling walls in detection of wideband signals from Bernoulli nonuniform sampling (BNS) in the presence of noise uncertainty. A sampling wall is defined as the sampling density below which the target error probabilities, i.e., the missed detection and false alarm probabilities, cannot be guaranteed at a given signal to noise ratio (SNR) regardless the number of acquired samples. The BNS is adopted because it exhibits good tradeoff properties between complexity and performance. It is shown that BNS suffers from noise enhancement, which translates into a whitening effect in the correlation of the legacy signal. Contrarily to the existing literature, the signal detection problem is addressed without having to reconstruct neither the signal nor its spectrum. More specifically, the optimal low SNR detector is formulated as a generalized likelihood ratio test (GLRT) to exploit the available side information of the problem, i.e., the noise variance, the sampling density and the legacy signal autocorrelation. By deriving the asymptotic performance of the GLRT in the presence of noise uncertainty, explicit expressions for sampling walls are obtained as a function of the legacy signal occupancy, the SNR and the noise uncertainty. Further, numerical results are provided to assess the behavior of the sampling walls and signal detection performance.
20150318T12:50:40Z
Font Segura, Josep
Vázquez Grau, Gregorio
Riba Sagarra, Jaume
This work shows the existence of sampling walls in detection of wideband signals from Bernoulli nonuniform sampling (BNS) in the presence of noise uncertainty. A sampling wall is defined as the sampling density below which the target error probabilities, i.e., the missed detection and false alarm probabilities, cannot be guaranteed at a given signal to noise ratio (SNR) regardless the number of acquired samples. The BNS is adopted because it exhibits good tradeoff properties between complexity and performance. It is shown that BNS suffers from noise enhancement, which translates into a whitening effect in the correlation of the legacy signal. Contrarily to the existing literature, the signal detection problem is addressed without having to reconstruct neither the signal nor its spectrum. More specifically, the optimal low SNR detector is formulated as a generalized likelihood ratio test (GLRT) to exploit the available side information of the problem, i.e., the noise variance, the sampling density and the legacy signal autocorrelation. By deriving the asymptotic performance of the GLRT in the presence of noise uncertainty, explicit expressions for sampling walls are obtained as a function of the legacy signal occupancy, the SNR and the noise uncertainty. Further, numerical results are provided to assess the behavior of the sampling walls and signal detection performance.

The DoF of the 3User (p, p+1) MIMO Interference Channel
http://hdl.handle.net/2117/25955
The DoF of the 3User (p, p+1) MIMO Interference Channel
Torrellas Socastro, Marc; Agustín de Dios, Adrián; Vidal Manzano, José; Muñoz Medina, Olga
The degrees of freedom (DoF) of the 3user multipleinput multipleoutput (MIMO) interference channel (IC) with full channel state information and constant channel coefficients are investigated when (p, p + 1) antennas are deployed at the transmitters and receivers, respectively. The point of departure of this paper is the work of Wang et al., which conjectured but did not prove the DoF for the antenna settings with p > 1. Here the achievability of the DoF outer bound is formally proved using linear methods, thereby avoiding the use of the rational dimensions framework. The proposed transmission scheme exploits asymmetric complex signaling together with symbol extensions in time and space interference alignment concepts. While the paper deals with the p = 2, 3, ... , 6 cases, providing the specific transmit and receive filters, there are also provided the tools needed for proving the achievability of the optimal DoF for p > 6, whose DoF characterization is conjectured.
20150119T16:22:41Z
Torrellas Socastro, Marc
Agustín de Dios, Adrián
Vidal Manzano, José
Muñoz Medina, Olga
The degrees of freedom (DoF) of the 3user multipleinput multipleoutput (MIMO) interference channel (IC) with full channel state information and constant channel coefficients are investigated when (p, p + 1) antennas are deployed at the transmitters and receivers, respectively. The point of departure of this paper is the work of Wang et al., which conjectured but did not prove the DoF for the antenna settings with p > 1. Here the achievability of the DoF outer bound is formally proved using linear methods, thereby avoiding the use of the rational dimensions framework. The proposed transmission scheme exploits asymmetric complex signaling together with symbol extensions in time and space interference alignment concepts. While the paper deals with the p = 2, 3, ... , 6 cases, providing the specific transmit and receive filters, there are also provided the tools needed for proving the achievability of the optimal DoF for p > 6, whose DoF characterization is conjectured.

Single and multifrequency wideband spectrum sensing with sideinformation
http://hdl.handle.net/2117/25065
Single and multifrequency wideband spectrum sensing with sideinformation
Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
This study addresses the optimal spectrum sensing detection based on the complete or partial sideinformation on the signal and noise statistics. The use of the generalisedlikelihood ratio test (GLRT) involves maximumlikelihood (ML) estimation of the nuisances. ML estimation of the unknowns is especially challenging for wideband cognitive radio because closedform solutions are often not available. Based on the equivalence between the wideband regime and the lowsignaltonoise ratio regime, this study provides a general kernel framework for GLRT spectrum sensing. It is shown that any GLRT detector exclusively depends on the projection of the sample covariance matrix of the data onto a given underlying kernel that reflects the available sideinformation in the problem. The kernels in several scenarios of interest are derived, including the widespread single and multifrequency channelisation cases. Theoretical interpretations and numerical results show the tradeoff between detection performance and the degree of sideinformation on the most informative statistics for detection, that is, the modulation format and spectrum distribution of the primary users.
20141217T15:46:43Z
Font Segura, Josep
Vázquez Grau, Gregorio
Riba Sagarra, Jaume
This study addresses the optimal spectrum sensing detection based on the complete or partial sideinformation on the signal and noise statistics. The use of the generalisedlikelihood ratio test (GLRT) involves maximumlikelihood (ML) estimation of the nuisances. ML estimation of the unknowns is especially challenging for wideband cognitive radio because closedform solutions are often not available. Based on the equivalence between the wideband regime and the lowsignaltonoise ratio regime, this study provides a general kernel framework for GLRT spectrum sensing. It is shown that any GLRT detector exclusively depends on the projection of the sample covariance matrix of the data onto a given underlying kernel that reflects the available sideinformation in the problem. The kernels in several scenarios of interest are derived, including the widespread single and multifrequency channelisation cases. Theoretical interpretations and numerical results show the tradeoff between detection performance and the degree of sideinformation on the most informative statistics for detection, that is, the modulation format and spectrum distribution of the primary users.

Asymptotically optimal linear shrinkage of sample LMMSE and MVDR filters
http://hdl.handle.net/2117/24612
Asymptotically optimal linear shrinkage of sample LMMSE and MVDR filters
Serra, Jordi; Nájar Martón, Montserrat
Conventional implementations of the linearminimum meansquare (LMMSE) and minimum variance distortionless response (MVDR) estimators rely on the sample matrix inversion (SMI) technique, i.e., on the sample covariance matrix (SCM). This approach is optimal in the large sample size regime. Nonetheless, in small sample size situations, those sample estimators suffer a large performance degradation. Thus, the aim of this paper is to propose corrections of these sample methods that counteract their performance degradation in the small sample size regime and keep their optimality in large sample size situations. To this aim, a twofold approach is proposed. First, shrinkage estimators are considered, as they are known to be robust to the small sample size regime. Namely, the proposed methods are based on shrinking the sample LMMSE or sample MVDR filters towards a variously called matched filter or conventional (Bartlett) beamformer in array processing. Second, random matrix theory is used to obtain the optimal shrinkage factors for large filters. The simulation results highlight that the proposed methods outperform the sample LMMSE and MVDR. Also, provided that the sample size is higher than the observation dimension, they improve classical diagonal loading (DL) and LedoitWolf (LW) techniques, which counteract the small sample size degradation by regularizing the SCM. Finally, compared to stateoftheart DL, the proposed methods reduce the computational cost and the proposed shrinkage of the LMMSE obtains performance gains.
20141107T15:44:05Z
Serra, Jordi
Nájar Martón, Montserrat
Conventional implementations of the linearminimum meansquare (LMMSE) and minimum variance distortionless response (MVDR) estimators rely on the sample matrix inversion (SMI) technique, i.e., on the sample covariance matrix (SCM). This approach is optimal in the large sample size regime. Nonetheless, in small sample size situations, those sample estimators suffer a large performance degradation. Thus, the aim of this paper is to propose corrections of these sample methods that counteract their performance degradation in the small sample size regime and keep their optimality in large sample size situations. To this aim, a twofold approach is proposed. First, shrinkage estimators are considered, as they are known to be robust to the small sample size regime. Namely, the proposed methods are based on shrinking the sample LMMSE or sample MVDR filters towards a variously called matched filter or conventional (Bartlett) beamformer in array processing. Second, random matrix theory is used to obtain the optimal shrinkage factors for large filters. The simulation results highlight that the proposed methods outperform the sample LMMSE and MVDR. Also, provided that the sample size is higher than the observation dimension, they improve classical diagonal loading (DL) and LedoitWolf (LW) techniques, which counteract the small sample size degradation by regularizing the SCM. Finally, compared to stateoftheart DL, the proposed methods reduce the computational cost and the proposed shrinkage of the LMMSE obtains performance gains.

Pattern matching for building feature extraction
http://hdl.handle.net/2117/24251
Pattern matching for building feature extraction
Lagunas Targarona, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar Martón, Montserrat
We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to throughthewall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The featurebased nature of the proposed detector enables corner separation from other indoor scatterers, such as humans. © 20042012 IEEE.
20141003T17:04:55Z
Lagunas Targarona, Eva
Amin, Moeness G.
Ahmad, Fauzia
Nájar Martón, Montserrat
We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to throughthewall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The featurebased nature of the proposed detector enables corner separation from other indoor scatterers, such as humans. © 20042012 IEEE.

Energyaware broadcast multiuserMIMO precoder design with imperfect channel and battery knowledge
http://hdl.handle.net/2117/23699
Energyaware broadcast multiuserMIMO precoder design with imperfect channel and battery knowledge
Rubio López, Javier; Pascual Iserte, Antonio
This paper addresses the problem of resource allocation and precoder design in a multiuser MIMO broadcast system where the terminals are batterypowered devices provided with energy harvesting capabilities. Energy harvesting is a promising technology based on which it is possible to recharge the battery of the terminals using energy collected from the environment. Models for the power consumption of the frontend and decoding stages are discussed and included in the design of the proposed scheme. In addition, the information concerning the battery level plays an explicit role and has an impact on the design of our proposed allocation strategy. Sumrate maximization is considered as optimization policy and energyrelated constraints are taken into account explicitly in the resource allocation in order to increase the lifetime of the batteries. In the first part of the paper, we consider the transmitter to have perfect channel state information (CSI) and battery knowledge, assuming an ideal feedback link. Then, a robust design based on imperfect channel information at the transmitter is studied and its effect on the energy consumed by the terminals is analyzed. Finally, an extended robust approach considering also imperfect battery knowledge (quantized battery status) at the transmitter is also addressed. Simulation results show that our proposed technique not only improves the usage of the batteries when compared with other traditional allocation policies, but also enhances the average data rate
20140901T09:37:33Z
Rubio López, Javier
Pascual Iserte, Antonio
This paper addresses the problem of resource allocation and precoder design in a multiuser MIMO broadcast system where the terminals are batterypowered devices provided with energy harvesting capabilities. Energy harvesting is a promising technology based on which it is possible to recharge the battery of the terminals using energy collected from the environment. Models for the power consumption of the frontend and decoding stages are discussed and included in the design of the proposed scheme. In addition, the information concerning the battery level plays an explicit role and has an impact on the design of our proposed allocation strategy. Sumrate maximization is considered as optimization policy and energyrelated constraints are taken into account explicitly in the resource allocation in order to increase the lifetime of the batteries. In the first part of the paper, we consider the transmitter to have perfect channel state information (CSI) and battery knowledge, assuming an ideal feedback link. Then, a robust design based on imperfect channel information at the transmitter is studied and its effect on the energy consumed by the terminals is analyzed. Finally, an extended robust approach considering also imperfect battery knowledge (quantized battery status) at the transmitter is also addressed. Simulation results show that our proposed technique not only improves the usage of the batteries when compared with other traditional allocation policies, but also enhances the average data rate

A ratesplitting approach to fading channels with imperfect channelstate information
http://hdl.handle.net/2117/23656
A ratesplitting approach to fading channels with imperfect channelstate information
Pastore, Adriano; Koch, Tobias; Rodríguez Fonollosa, Javier
As shown by Médard, the capacity of fading channels with imperfect channelstate information can be lowerbounded by assuming a Gaussian channel input X with power P and by upperbounding the conditional entropy h(XY,H) by the entropy of a Gaussian random variable with variance equal to the linear minimum meansquare error in estimating X from \(Y, H). We demonstrate that, using a ratesplitting approach, this lower bound can be sharpened: by expressing the Gaussian input X as the sum of two independent Gaussian variables X1 and X2 and by applying Médard's lower bound first to bound the mutual information between X1 and Y while treating X2 as noise, and by applying it a second time to the mutual information between X2 and Y while assuming X1 to be known, we obtain a capacity lower bound that is strictly larger than Médard's lower bound. We then generalize this approach to an arbitrary number L of layers, where X is expressed as the sum of L independent Gaussian random variables of respective variances Pl, l = 1, ¿ ,L summing up to P. Among all such ratesplitting bounds, we determine the supremum over power allocations Pl and total number of layers L. This supremum is achieved for L 8 and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussianinput mutual information as the signaltonoise ratio (SNR) grows, provided that the variance of the channel estimation error HH tends to zero as the SNR tends to infinity.
20140731T09:02:21Z
Pastore, Adriano
Koch, Tobias
Rodríguez Fonollosa, Javier
As shown by Médard, the capacity of fading channels with imperfect channelstate information can be lowerbounded by assuming a Gaussian channel input X with power P and by upperbounding the conditional entropy h(XY,H) by the entropy of a Gaussian random variable with variance equal to the linear minimum meansquare error in estimating X from \(Y, H). We demonstrate that, using a ratesplitting approach, this lower bound can be sharpened: by expressing the Gaussian input X as the sum of two independent Gaussian variables X1 and X2 and by applying Médard's lower bound first to bound the mutual information between X1 and Y while treating X2 as noise, and by applying it a second time to the mutual information between X2 and Y while assuming X1 to be known, we obtain a capacity lower bound that is strictly larger than Médard's lower bound. We then generalize this approach to an arbitrary number L of layers, where X is expressed as the sum of L independent Gaussian random variables of respective variances Pl, l = 1, ¿ ,L summing up to P. Among all such ratesplitting bounds, we determine the supremum over power allocations Pl and total number of layers L. This supremum is achieved for L 8 and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussianinput mutual information as the signaltonoise ratio (SNR) grows, provided that the variance of the channel estimation error HH tends to zero as the SNR tends to infinity.

Noncooperative dayahead bidding strategies for demandside expected cost minimization with realtime adjustments: a GNEP approach
http://hdl.handle.net/2117/23344
Noncooperative dayahead bidding strategies for demandside expected cost minimization with realtime adjustments: a GNEP approach
Atzeni, Italo; García Ordoñez, Luis; Scutari, Gesualdo; Palomar, Daniel P.; Rodríguez Fonollosa, Javier
The envisioned smart grid aims at improving the interaction between the supplyand the demandside of the electricity network, creating unprecedented possibilities for optimizing the energy usage at different levels of the grid. In this paper, we propose a distributed demandside management (DSM) method intended for smart grid users with load prediction capabilities, who possibly employ dispatchable energy generation and storage devices. These users participate in the dayahead market and are interested in deriving the bidding, production, and storage strategies that jointly minimize their expected monetary expense. The resulting dayahead grid optimization is formulated as a generalized Nash equilibrium problem (GNEP), which includes global constraints that couple the users' strategies. Building on the theory of variational inequalities, we study the main properties of the GNEP and devise a distributed, iterative algorithm converging to the variational solutions of the GNEP. Additionally, users can exploit the reduced uncertainty about their energy consumption and renewable generation at the time of dispatch. We thus present a complementary DSM procedure that allows them to perform some unilateral adjustments on their generation and storage strategies so as to reduce the impact of their realtime deviations with respect to the amount of energy negotiated in the dayahead. Finally, numerical results in realistic scenarios are reported to corroborate the proposed DSM technique.
20140701T08:28:06Z
Atzeni, Italo
García Ordoñez, Luis
Scutari, Gesualdo
Palomar, Daniel P.
Rodríguez Fonollosa, Javier
The envisioned smart grid aims at improving the interaction between the supplyand the demandside of the electricity network, creating unprecedented possibilities for optimizing the energy usage at different levels of the grid. In this paper, we propose a distributed demandside management (DSM) method intended for smart grid users with load prediction capabilities, who possibly employ dispatchable energy generation and storage devices. These users participate in the dayahead market and are interested in deriving the bidding, production, and storage strategies that jointly minimize their expected monetary expense. The resulting dayahead grid optimization is formulated as a generalized Nash equilibrium problem (GNEP), which includes global constraints that couple the users' strategies. Building on the theory of variational inequalities, we study the main properties of the GNEP and devise a distributed, iterative algorithm converging to the variational solutions of the GNEP. Additionally, users can exploit the reduced uncertainty about their energy consumption and renewable generation at the time of dispatch. We thus present a complementary DSM procedure that allows them to perform some unilateral adjustments on their generation and storage strategies so as to reduce the impact of their realtime deviations with respect to the amount of energy negotiated in the dayahead. Finally, numerical results in realistic scenarios are reported to corroborate the proposed DSM technique.