In this correspondence, the achievable rates of the so called "multiple-input multiple-output interference channel," exploited by a couple of single antenna primary terminals and two antenna cognitive radios under specific interference constraints, are analyzed. In particular, by assuming perfect channel state information at the cognitive terminals, a closed form expression for a linear precoding and linear reception scheme, which guarantees to meet the achievable rates and no mutual interference between primary and cognitive terminals, is obtained. Numerical results regarding the effects of different fading channels and of an imperfect knowledge of the channel are provided to evaluate the performances of the proposed scheme in real environments.

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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.

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A mathematical modeling approach for elastic scattering and light propagation is presented, which can be used to obtain the scattering coefficient, the index of refraction, and the distribution of the collagen fibrils in a gel. Collagen fibrils can be realistically represented by small cylindrical particles. The analysis of the scattering of light by such particles provides the scattering coefficient. Light transport in multilayered tissues has been modeled and the collagen fibrils scattering coefficient has been considered as main input parameters. Assuming that a gel is composed of fibrils with the same diameter, it is possible to obtain all the input parameters of the model and, therefore, a simulated spectrum. This can be repeated for several diameters. Considering a gel composed of fibrils with different diameters, it is possible to obtain a best-fitting simulated spectrum as a weighted sum (least-square-error based) of the spectra corresponding to several fibril diameters, and, therefore, obtain an estimate of the percentages of fibrils of each diameter in the gel. Moreover, the scattering coefficient and refractive index, which are also provided by the model, are relevant parameters as they relate to tissue properties in their own right.

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.

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.