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http://hdl.handle.net/2117/1256
Tue, 07 Jul 2015 07:16:33 GMT
20150707T07:16:33Z
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Nonuniform sampling walls in wideband signal detection
http://hdl.handle.net/2117/26802
Title: Nonuniform sampling walls in wideband signal detection
Authors: Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
Abstract: 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.
Wed, 18 Mar 2015 12:50:40 GMT
http://hdl.handle.net/2117/26802
20150318T12:50:40Z
Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
no
Bernoulli nonuniform sampling, Cognitive radio, GLRT, Sampling walls, SNR walls, Cognitive radios, Sprectrum, Information
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
Title: The DoF of the 3User (p, p+1) MIMO Interference Channel
Authors: Torrellas Socastro, Marc; Agustín de Dios, Adrián; Vidal Manzano, José; Muñoz Medina, Olga
Abstract: 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.
Mon, 19 Jan 2015 16:22:41 GMT
http://hdl.handle.net/2117/25955
20150119T16:22:41Z
Torrellas Socastro, Marc; Agustín de Dios, Adrián; Vidal Manzano, José; Muñoz Medina, Olga
no
Interference channels, MIMO, Interference alignment, Degrees of freedom, User scaling law, Broadcast channels, XChannels, Aligment, Freedom
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
Title: Single and multifrequency wideband spectrum sensing with sideinformation
Authors: Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
Abstract: 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.
Wed, 17 Dec 2014 15:46:43 GMT
http://hdl.handle.net/2117/25065
20141217T15:46:43Z
Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
no
Cognitive radio, Radio spectrum management, Signal detection, Maximum likelihood estimation, Statistical testing, Covariance matrices, Multifrequency wideband spectrum sensing, Optimal spectrum sensing detection, Partial sideinformation, Noise statistics, Generalisedlikelihood ratio test, Maximumlikelihood estimation, ML estimation, Wideband cognitive radio, Closedform solutions, Lowsignaltonoise ratio, General kernel framework, GLRT detector, Sample covariance matrix projection, Multifrequency channelisation, Informative statistics, Modulation format, Spectrum distribution, Cognitive radio networks
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
Title: Asymptotically optimal linear shrinkage of sample LMMSE and MVDR filters
Authors: Serra, Jordi; Nájar Martón, Montserrat
Abstract: 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.
Fri, 07 Nov 2014 15:44:05 GMT
http://hdl.handle.net/2117/24612
20141107T15:44:05Z
Serra, Jordi; Nájar Martón, Montserrat
no
Shrinkage estimation, Random matrix theory, Consistent estimation, LMMSE, MVDR, Covariance matrices, Waveform, Eigenvectors, Eigenvalues, Estimators, Signal
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
Title: Pattern matching for building feature extraction
Authors: Lagunas Targarona, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar Martón, Montserrat
Abstract: 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.
Fri, 03 Oct 2014 17:04:55 GMT
http://hdl.handle.net/2117/24251
20141003T17:04:55Z
Lagunas Targarona, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar Martón, Montserrat
no
Building dominant scatterers, Pattern matching, Throughthewall radar imaging (TWRI)
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
Title: Energyaware broadcast multiuserMIMO precoder design with imperfect channel and battery knowledge
Authors: Rubio López, Javier; Pascual Iserte, Antonio
Abstract: 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
Mon, 01 Sep 2014 09:37:33 GMT
http://hdl.handle.net/2117/23699
20140901T09:37:33Z
Rubio López, Javier; Pascual Iserte, Antonio
no
Battery information, Battery quantization, Channel estimation errors, Energy harvesting, MIMO system, Power consumption, Robust design
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
Title: A ratesplitting approach to fading channels with imperfect channelstate information
Authors: Pastore, Adriano; Koch, Tobias; Rodríguez Fonollosa, Javier
Abstract: 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.
Thu, 31 Jul 2014 09:02:21 GMT
http://hdl.handle.net/2117/23656
20140731T09:02:21Z
Pastore, Adriano; Koch, Tobias; Rodríguez Fonollosa, Javier
no
Channel capacity, Fading channels, Flat fading, Imperfect channelstate information
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
Title: Noncooperative dayahead bidding strategies for demandside expected cost minimization with realtime adjustments: a GNEP approach
Authors: Atzeni, Italo; García Ordoñez, Luis; Scutari, Gesualdo; Palomar, Daniel P.; Rodríguez Fonollosa, Javier
Abstract: 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.
Tue, 01 Jul 2014 08:28:06 GMT
http://hdl.handle.net/2117/23344
20140701T08:28:06Z
Atzeni, Italo; García Ordoñez, Luis; Scutari, Gesualdo; Palomar, Daniel P.; Rodríguez Fonollosa, Javier
no
Dayahead/realtime demandside management, Game theory, Generalized Nash equilibrium problem, Proximal decomposition algorithm, Smart grid, Variational inequality
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.

Frequencydomain GLR detection of a secondorder cyclostationary signal over fading channels
http://hdl.handle.net/2117/22790
Title: Frequencydomain GLR detection of a secondorder cyclostationary signal over fading channels
Authors: Riba Sagarra, Jaume; Font Segura, Josep; Villares Piera, Nemesio J.; Vázquez Grau, Gregorio
Abstract: Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank1 frequencydomain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of 1/T. By resorting to the fast Fourier transform (FFT), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the wellknown sensitivity of cyclostationarybased detection schemes to frequencyselective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector.
Wed, 30 Apr 2014 12:40:12 GMT
http://hdl.handle.net/2117/22790
20140430T12:40:12Z
Riba Sagarra, Jaume; Font Segura, Josep; Villares Piera, Nemesio J.; Vázquez Grau, Gregorio
no
GLRT, LMPIT, Cognitive radio, Cyclostationarity based detection, Spectral correlation, Timing synchronization, Frequencysmoothed cyclic periodogram, Maximumlikelyhood, Component, Tests, Noise
Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank1 frequencydomain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of 1/T. By resorting to the fast Fourier transform (FFT), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the wellknown sensitivity of cyclostationarybased detection schemes to frequencyselective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector.

Radio context awareness and applications
http://hdl.handle.net/2117/21937
Title: Radio context awareness and applications
Authors: Reggiani, Luca; Fiorina, Jocelyn; Gezici, Sinan; Morosi, Simone; Nájar Martón, Montserrat
Abstract: The context refers to “any information that can be used to characterize the situation of an entity, where an entity can be a person, place, or physical object.” Radio context awareness is defined as the ability of detecting and estimating a system state or parameter, either globally or concerning one of its components, in a radio system for enhancing performance at the physical, network, or application layers. In this paper, we review the fundamentals of context awareness and the recent advances in the main radio techniques that increase the context awareness and smartness, posing challenges and renewed opportunities to addedvalue applications in the context of the next generation of wireless networks.
Fri, 07 Mar 2014 14:41:47 GMT
http://hdl.handle.net/2117/21937
20140307T14:41:47Z
Reggiani, Luca; Fiorina, Jocelyn; Gezici, Sinan; Morosi, Simone; Nájar Martón, Montserrat
no
The context refers to “any information that can be used to characterize the situation of an entity, where an entity can be a person, place, or physical object.” Radio context awareness is defined as the ability of detecting and estimating a system state or parameter, either globally or concerning one of its components, in a radio system for enhancing performance at the physical, network, or application layers. In this paper, we review the fundamentals of context awareness and the recent advances in the main radio techniques that increase the context awareness and smartness, posing challenges and renewed opportunities to addedvalue applications in the context of the next generation of wireless networks.

A nondataaided SNR estimation technique for multilevel modulations exploiting signal cyclostationarity
http://hdl.handle.net/2117/21450
Title: A nondataaided SNR estimation technique for multilevel modulations exploiting signal cyclostationarity
Authors: Riba Sagarra, Jaume; Villares Piera, Nemesio J.; Vázquez Grau, Gregorio
Abstract: Signaltonoise ratio (SNR) estimators of linear
modulation schemes usually operate at one sample per symbol at
the matched filter output. In this paper we propose a new method
for estimating the SNR in the complex additive white Gaussian
noise (AWGN) channel that operates directly on the oversampled
cyclostationary signal at the matched filter input. Exploiting
cyclostationarity proves to be advantageous due to the fact that a
signalfree Euclidean noise subspace can be identified such that
only second order moments of the received waveform need to be
computed. The proposed method is nondataaided (NDA), as well
as constellation and phase independent, and only requires prior
timing synchronization to fully exploit the cyclostationarity property.
The estimator can also be applied to nonconstant modulus
constellations without requiring any tuning, which is a feature not
found in existing approaches. Implementation aspects and simpler
suboptimal solutions are also provided.
Tue, 04 Feb 2014 14:32:34 GMT
http://hdl.handle.net/2117/21450
20140204T14:32:34Z
Riba Sagarra, Jaume; Villares Piera, Nemesio J.; Vázquez Grau, Gregorio
no
Cyclostationarity, SNR estimation, Secondorder methods, Spectral coherence, Rate of innovation
Signaltonoise ratio (SNR) estimators of linear
modulation schemes usually operate at one sample per symbol at
the matched filter output. In this paper we propose a new method
for estimating the SNR in the complex additive white Gaussian
noise (AWGN) channel that operates directly on the oversampled
cyclostationary signal at the matched filter input. Exploiting
cyclostationarity proves to be advantageous due to the fact that a
signalfree Euclidean noise subspace can be identified such that
only second order moments of the received waveform need to be
computed. The proposed method is nondataaided (NDA), as well
as constellation and phase independent, and only requires prior
timing synchronization to fully exploit the cyclostationarity property.
The estimator can also be applied to nonconstant modulus
constellations without requiring any tuning, which is a feature not
found in existing approaches. Implementation aspects and simpler
suboptimal solutions are also provided.

Spectrum sensing using correlated receiving multiple antennas in cognitive radios
http://hdl.handle.net/2117/21049
Title: Spectrum sensing using correlated receiving multiple antennas in cognitive radios
Authors: Sedighi, Saeid; Taherpour, Abbas; Sala Álvarez, José
Abstract: Spectrum sensing is a key component for enabling the cognitive radio paradigm. In this paper, we propose a novel totallyblind spectrum sensing technique for cognitive radio device equipped with multiple antennas, namely the Space Frequency Cross Product Sensing (SFCPS) algorithm. Existing correlationbased spectrum sensing techniques rely on the assumption that the received signals are correlated and their performance becomes poor when the signal correlation is low. By appropriately combining the received signals from multiple antennas, the proposed method creates new signals that are fully correlated and on which a sensing method is developed. SFCPS performs better than existing correlationbased techniques and with a lower computational complexity for small number of observed samples.
Wed, 18 Dec 2013 15:39:20 GMT
http://hdl.handle.net/2117/21049
20131218T15:39:20Z
Sedighi, Saeid; Taherpour, Abbas; Sala Álvarez, José
no
Cognitive radio, Spectrum sensing, Multiple antennas, Rao test, Antenna correlations, Fisher information matrix, Noise variance mismatch, Antenna array
Spectrum sensing is a key component for enabling the cognitive radio paradigm. In this paper, we propose a novel totallyblind spectrum sensing technique for cognitive radio device equipped with multiple antennas, namely the Space Frequency Cross Product Sensing (SFCPS) algorithm. Existing correlationbased spectrum sensing techniques rely on the assumption that the received signals are correlated and their performance becomes poor when the signal correlation is low. By appropriately combining the received signals from multiple antennas, the proposed method creates new signals that are fully correlated and on which a sensing method is developed. SFCPS performs better than existing correlationbased techniques and with a lower computational complexity for small number of observed samples.

Array gain in the DMT framework for MIMO channels
http://hdl.handle.net/2117/17178
Title: Array gain in the DMT framework for MIMO channels
Authors: García Ordoñez, Luis; Palomar, D.P.; Rodríguez Fonollosa, Javier
Abstract: Following the seminal work by Zheng and Tse on
the diversity and multiplexing tradeoff (DMT) of multipleinput
multipleoutput (MIMO) channels, in this paper, we introduce
the array gain to investigate the fundamental relation between
transmission rate and reliability inMIMO systems. The array gain
gives information on the power offset that results from exploiting
channel state information at the transmitter or as a consequence
of the channel model. Hence, the diversity, multiplexing, and
array gain (DMA) analysis is able to cope with the limitations
of the original DMT and provide an operational meaning in the
sense that the DMA gains of a particular system can be directly
translated into a parameterized characterization of its associated
outage probability performance. In this paper, we derive the
best DMA gains achievable by any scheme employing isotropic
signaling in uncorrelated Rayleigh, semicorrelated Rayleigh, and
uncorrelated Rician blockfading MIMO channels. We use these
results to analyze the effect of important channel parameters on
the outage performance at different points of the DMT curve.
Thu, 20 Dec 2012 14:31:52 GMT
http://hdl.handle.net/2117/17178
20121220T14:31:52Z
García Ordoñez, Luis; Palomar, D.P.; Rodríguez Fonollosa, Javier
no
Array gain, diversity multiplexing tradeoff (DMT), outage probability, performance analysis of multipleinput multipleoutput (MIMO) channels
Following the seminal work by Zheng and Tse on
the diversity and multiplexing tradeoff (DMT) of multipleinput
multipleoutput (MIMO) channels, in this paper, we introduce
the array gain to investigate the fundamental relation between
transmission rate and reliability inMIMO systems. The array gain
gives information on the power offset that results from exploiting
channel state information at the transmitter or as a consequence
of the channel model. Hence, the diversity, multiplexing, and
array gain (DMA) analysis is able to cope with the limitations
of the original DMT and provide an operational meaning in the
sense that the DMA gains of a particular system can be directly
translated into a parameterized characterization of its associated
outage probability performance. In this paper, we derive the
best DMA gains achievable by any scheme employing isotropic
signaling in uncorrelated Rayleigh, semicorrelated Rayleigh, and
uncorrelated Rician blockfading MIMO channels. We use these
results to analyze the effect of important channel parameters on
the outage performance at different points of the DMT curve.

Downlink coordinated radio resource management in cellular networks with partial CSI
http://hdl.handle.net/2117/16625
Title: Downlink coordinated radio resource management in cellular networks with partial CSI
Authors: Calvo Page, Eduard; Muñoz Medina, Olga; Vidal Manzano, José; Agustín de Dios, Adrián
Abstract: We explore decentralized coordination of sectored
cellular networks to adapt the usage of downlink resources to
the instantaneous network conditions. The transmission frame
consists of an orthogonal bandwidth usage phase, where sectors
perform FDMA and power control over an agreed frequency
chunk, and a shared bandwidth usage phase where each sector
performs FDMA over the full available bandwidth without power
control (interference is not controlled in this phase by any means).
Decentralized network utility maximization with global optimality
guarantee is enabled by fixing this structure of the transmission
frame, which does not cause significant networkwide losses. Thus,
the ability to better balance the resources gained from coordination
generates some slack that can be used to either i) provide
higherquality access, ii) increase the number of active users, or
iii) reduce deployment and maintenance costs by operating larger
cells.
Wed, 03 Oct 2012 17:49:18 GMT
http://hdl.handle.net/2117/16625
20121003T17:49:18Z
Calvo Page, Eduard; Muñoz Medina, Olga; Vidal Manzano, José; Agustín de Dios, Adrián
no
Coordinated interference mitigation, distributed algorithms, multicell resource allocation, network utility maximization
We explore decentralized coordination of sectored
cellular networks to adapt the usage of downlink resources to
the instantaneous network conditions. The transmission frame
consists of an orthogonal bandwidth usage phase, where sectors
perform FDMA and power control over an agreed frequency
chunk, and a shared bandwidth usage phase where each sector
performs FDMA over the full available bandwidth without power
control (interference is not controlled in this phase by any means).
Decentralized network utility maximization with global optimality
guarantee is enabled by fixing this structure of the transmission
frame, which does not cause significant networkwide losses. Thus,
the ability to better balance the resources gained from coordination
generates some slack that can be used to either i) provide
higherquality access, ii) increase the number of active users, or
iii) reduce deployment and maintenance costs by operating larger
cells.

Asymptotically optimum energy profile for successive interference cancellation in DSCDMA under a power unbalance constraint
http://hdl.handle.net/2117/15654
Title: Asymptotically optimum energy profile for successive interference cancellation in DSCDMA under a power unbalance constraint
Authors: Sala Álvarez, José; Rey Micolau, Francesc; Villares Piera, Nemesio J.
Abstract: A new Ordinary Differential Equation (ODE) governing the SNIR evolution of a Successive Interference Canceller (SIC) for DSCDMA is derived when the number of users tends to infinity and all users share the same channel encoder. Using Variational Calculus, this ODE is applied to obtaining the energy profile that maximizes the average spectral efficiency when a constraint on the power unbalance (maximum power to minimum power ratio) of received users is enforced. The conditions for extremality of the optimum energy profile are established in terms of the common encoder's Packet Error Rate (PER) function.
Thu, 22 Mar 2012 19:25:00 GMT
http://hdl.handle.net/2117/15654
20120322T19:25:00Z
Sala Álvarez, José; Rey Micolau, Francesc; Villares Piera, Nemesio J.
no
successive interference cancellation
power unbalance
differential equation
variational calculus
packet error rate
CDMA
error propagation
A new Ordinary Differential Equation (ODE) governing the SNIR evolution of a Successive Interference Canceller (SIC) for DSCDMA is derived when the number of users tends to infinity and all users share the same channel encoder. Using Variational Calculus, this ODE is applied to obtaining the energy profile that maximizes the average spectral efficiency when a constraint on the power unbalance (maximum power to minimum power ratio) of received users is enforced. The conditions for extremality of the optimum energy profile are established in terms of the common encoder's Packet Error Rate (PER) function.