Comparison among Cognitive Radio Architectures for Spectrum Sensing

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed

Clinical features and cognitive sequelae in COVID-19: a retrospective study on N=152 patients

Background: The novel human coronavirus (SARS-CoV-2) shows neurotropism and systemically affects the central nervous system (CNS). Cognitive deficits have been indeed reported as both short- and long-term sequelae of SARS-CoV-2 infection. However, the association between these disturbances and background/disease-related clinical features remains elusive. This work aimed at exploring how post-infective cognitive status relates to clinical/treatment outcomes by controlling for premorbid/current risk factors for cognitive deficits. Methods: Cognitive measures (Mini-Mental State Examination, MMSE) of N=152 COVID-19 patient were retrospectively assessed in relation to disease severity, intensive care unit (ICU) admission, steroidal treatment, and occurrence of other viral/bacterial infections by controlling for remote/recent/COVID-19-related risk factors for cognitive deficits (at-risk vs. not-at-risk: Neuro+ vs. Neuro−). Results: Descriptively, impaired MMSE performances were highly prevalent in mild-to-moderate patients (26.3%). ICU-admitted patients made less errors (p=.021) on the MMSE than those not admitted when partialling out risk factors and age—the latter negatively influencing performances. When addressing Neuro− patients only, steroidal treatment appears to improve MMSE scores among those suffering from other infections (p=.025). Discussion: Cognitive sequelae of COVID-19 are likely to arise from a complex interplay between background/clinical premorbid features and disease-related/interventional procedures and outcomes. Mild-to-moderate patients requiring assistive ventilation who however are not admitted to an ICU are more likely to suffer from cognitive deficits—despite their etiology remaining elusive

p38 MAPK and JNK Antagonistically Control Senescence and Cytoplasmic p16INK4A Expression in Doxorubicin-Treated Endothelial Progenitor Cells

Patients treated with low-dose anthracyclines often show late onset cardiotoxicity. Recent studies suggest that this form of cardiotoxicity is the result of a progenitor cell disease. In this study we demonstrate that Cord Blood Endothelial Progenitor Cells (EPCs) exposed to low, sub-apoptotic doses of doxorubicin show a senescence phenotype characterized by increased SA-b-gal activity, decreased TRF2 and chromosomal abnormalities, enlarged cell shape, and disarrangement of F-actin stress fibers accompanied by impaired migratory ability. P16 INK4A localizes in the cytoplasm of doxorubicin-induced senescent EPCs and not in the nucleus as is the case in EPCs rendered senescent by different stimuli. This localization together with the presence of an arrest in G2, and not at the G1 phase boundary, which is what usually occurs in response to the cell cycle regulatory activity of p16INK4A, suggests that doxorubicin-induced p16 INK4A does not regulate the cell cycle, even though its increase is closely associated with senescence. The effects of doxorubicin are the result of the activation of MAPKs p38 and JNK which act antagonistically. JNK attenuates the senescence, p16 INK4A expression and cytoskeleton remodeling that are induced by activated p38. We also found that conditioned medium from doxorubicin-induced senescent cardiomyocytes does not attract untreated EPCs, unlike conditioned medium from apoptotic cardiomyocytes which has a strong chemoattractant capacity. In conclusion, this study provides a better understanding of the senescence of doxorubicin-treated EPCs, which may be helpful in preventing and treating late onset cardiotoxicity

Regularity of time-harmonic electromagnetic fields in the interior of bianisotropic materials and metamaterials

The regularity of the four time-harmonic vector fields composing any strong solution of the system obtained from Maxwell's curl equations and the constitutive relations in the interior of an inhomogeneous bianisotropic material are investigated. The results are given as interior Sobolev or Holder regularity. Possible local C-infinity regularity or local analyticity of the four vector fields are discussed, too. Each of these regularity results is obtained under specific conditions on the impressed current densities and on the constitutive parameters of the bianisotropic material considered, but it is shown that such conditions do not significantly limit the coverage of our analysis in terms of applications

OFDM recognition based on cyclostationary analysis in an open spectrum scenario

Abstract—In this paper the problem of detecting the presence of similar OFDM signals, i.e. WLAN and WiMAX signals, in an Open Spectrum scenario is faced. The identification of the channel occupancy and the signal classification are performed by using a fast detector based on a single spectral correlation function estimator and a multi-class support vector machine classifier which are designed and tested in a multipath environment. Finally, the obtained numerical results and the amount of processing necessary to perform the considered operations are reported and discussed. I

A comparison between stand-alone and distributed architectures for spectrum hole detection

Abstract—In this paper two different cognitive radio architectures, i.e. stand-alone and distributed, are proposed for spectrum sensing purposes. In particular, both architectures implement a fast and reliable algorithm based on cyclic features extraction which allows to identify spectrum holes. The performances of such systems are compared in detecting primary users ’ presence in a monitored area classifying the used transmission standards, IEEE 802.11a and IEEE 802.16e. The considered scenario is challenging since both standards use the OFDM transmission technique, are designed to have the same bandwidth and use the same frequency band. A set of numerical simulations have been carried out to compare the performances of the proposed systems in a heavy multipath scenario and their advantages and disadvantages are discussed. I

Signal classification based on spectral redundancy and neural network ensembles

Abstract—In the last couple of decades, the introduction of new wireless applications and services, which have to coexist with already deployed ones, is creating problems in the allocation of the unlicensed spectrum. In order to overcome such a problem, by exploiting efficiently the spectral resources, dynamic spectrum access has been proposed. In this context, cognitive radio represents one of the most promising technologies which allows an efficient use of the radio resource by collecting, processing and exploiting information regarding the spectrum utilization in a monitored area. To this end, in this paper the problem of classifying similar signals characterized by different spectral redundancies is addressed by using a neural network ensemble. A set of simulations have been carried out to prove the effectiveness of the considered algorithms and numerical results are reported. I