1,720,968 research outputs found
A distributed approach to mode identification and spectrum monitoring for cognitive radios
No full textIn this paper a distributed approach to mode identification and spectrum monitoring is studied. A Wireless Network composed by Cognitive Terminals is used to classify air interfaces present in the radio scene. The use of cooperative strategies and an advanced signal processing tool, Time Frequency analysis, allows to improve the radio awareness of device. Results in the terms of error probability, modeling the probability density function of considered features as Asymmetric Generalized and Generalized Gaussian functions, are compared to error rate showing good performance and coherence of theoretical model with experimental results
"Integration Between Navigation and Data-Transmission Systems in a Software Defined Radio Framework"
No full textIn the last years, an increasing interest of the market in devices able to integrate navigation and com-munication technologies has been noticed. Software Defined Radio technology helps in a tight integration of all possible radio signals into one, completely reconfigurable, universal device. In this work the problem of integration of Galileo, Satellite UMTS (S-UMTS) and IEEE 802.11b local wireless LAN has been faced through the usage of a multi-stage super-heterodyne analog front-end able to overlap the different signals into a reduced bandwidth. In this way it is possible the usage of low-cost components both for the analog heterodyne front and for the A/D Converter
"Neural networks based approach for data fusion in multi-frequency navigation receivers"
No full textIn this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented, The GPS system modernization phase and the Galileo system development will increase signal availability and hence GNSS system-based applications. This variety of uses pervades almost every aspect of GNSS activity and provides the stimulus for its future improvement. For all these causes, in the past few years, there has been a growing interest in the research on the development of techniques and methods for improving the signal reception. Unfortunately, the receiver measurement is usually affected by errors. As already said, in an urban environment the major error source is given by muttipath fading. The proposed method is based on frequency diversity, i.e. the distortions introduced by the channel can be considered different and uncorrelated for sufficiently spaced frequencies. For this reason it is possible to design receivers that, through the usage of multiple frequencies, can improve the reception of SIS, minimizing the distortion effects of the multipath channel. In the proposed system two frequencies in E band, i.e. E5A and E5B, have been considered, and the derived information is fused by using a Neural Network (NN). The NN bases its adaptive fusion on parameters which represent the amount of noise in each of the considered frequencies. Considering the receiver from an higher level, it would be more accurate and efficient if it would be provided by an artificial intelligence that can be developed within the framework of Cognitive Radio devices, the future paradigm for mobile navigation and communication terminals
"A hierarchical Neural Network-based receiver for GNSS systems"
No full textIn this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented. In particular a hierarchical neural network-based solution, able to estimate the channel in which the receiver operates, will be shown. The proposed method is based on two different Neural Networks and it is able to improve the fine tracking performances in urban environment. The solution takes advantage of the Self Organizing Map (SOM) properties, a particular type of Neural Networks useful in unsupervised systems, to improve the performances in presence of multipath
“A distributed wireless sensor network for radio scene analysis”
No full textIn this paper a distributed approach to mode identification is considered.
A Wireless Sensor Network composed by Software De-
fined and Cognitive terminals is used to classify air interfaces present
in the radio scene. Two modes, namely Frequency Hopping Code
Division Multiple Access and Direct Sequence Code Division Multiple
Access, are identified employing a signal processing technique,
Time Frequency analysis, and distributed decision theory.
Results in the terms of error probability are obtained, modeling the
probability density function of considered features as Asymmetric
Generalized and Generalized Gaussian functions
Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory
No full textIn Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New research trends for reaching this goal are based on neurophysiological observations of human brain structure and functioning. A learning technique that is used to provide the SS with the so-called Autobiographical Memory is presented here by drawing inspiration from a bio-inspired model of the interactions occurring between the system and the user. Starting from the hypothesis that user's actions have a direct influence on the internal system state variables and vice versa, a statistical voting algorithm is proposed for inferring the cause/effect relationships among users and the system. The main contribution of this paper lies in proposing a general framework that is able to allow the SS to be aware of its present state as well as of the behavior of its users and to be able to predict the expected consequences of user actions. In this paper, these concepts are explored in order to point out the relevant role of the structural coupling between the system and the environment (i.e., interaction with the user) in the development of context-aware SSs. In fact, a model is proposed here for learning in dynamic cognitive systems , which is able to understand the cause/effect relationships
between the changes in system state and the environmental perturbations that are due to the presence of the user. The algorithm that is based on neurophysiological studies on how human
self-consciousness arises and evolves is proposed to extract contextual information from heterogeneous sensor signals and to learn and predict interactions involving users that are present in an SS. The innovative aspect of this paper is the new way of modeling the interactions between the user and the system and its engineering implication in the development of context-aware learning/predicting strategies. To do so, multiple heterogeneous data coming from a set of sensors are jointly processed with the aim of detecting internal and external contextual events. Then, a nonparametric probabilistic interaction model is learned, which can be used to predict future events to be able to design anticipative decision strategies. The proposed mechanism, applied for processing and passively learning interactions between the system and the users, introduces new functionalities and modeling capabilities which can be exploited in SS design. This paper is organized as follows. In Section II, an approach for learning interactions between SSs and users, which is inspired by neurophysiological studies, is proposed. A procedure is introduced for using these memorized data to predict changes in the system’s internal status, which are caused by user interactions, potentially allowing self-reaction and self-adaptation capabilities. In Section III, the described algorithms are extensively tested in the scenario of an SS, which is installed in a university laboratory and which monitors the internal status of its devices and the external events produced by user actions. Section IV presents the comparisons with other learning approaches for AmI applications. Finally, in Section V, we conclude by commenting on open issues and possible future improvements
Video-radio fusion approach for target tracking in smart spaces
No full textSmart Spaces are an emerging technology which is gathering interest in several domains of application since they allow to supply services and to interact with users in a pervasive way. One of their basic tasks regards the localization of the users in order to provide services in a personalized and location- based way. However since the guarded area is usually complex (e.g. with occlusions) and extent several sensors must be used. In this work video data acquired by video-cameras and radio signals of the WLAN, by which user can access to services, are jointly employed to improve the association between video track and radio identifier, and, through a two step temporal filtering, it is possible to enhance the system reliability. Results are presented in a simulated environment showing the effectiveness of the proposed approach
HOS-based mode classification for infomobility framework
No full textThe growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. A possible solution for increasing the frequency reusage within the framework of info-mobility cellular systems is the joint exploitation of Smart Antennas and Cognitive Radio. Inside this framework a key-role is played by Mode Identification and Spectrum monitoring algorithms, useful to provide awareness about the channel conditions. In the paper a Mode Identification algorithm, based on the extraction of higher order statistics from frequency distribution of the involved communication modalities and multiple support vector machine classifiers, for a Cognitive Base Transceiver Station is presented. Simulated results, obtained in a simplified framework, will prove the effectiveness of the proposed approach
Architettura Basata su Cognitività Embodied per Antenne Intelligenti di Prossima Generazione
No full textSommario—In questo contributo `e descritta un’architettura
per la gestione di antenne intelligenti basata sul paradigma dei
sistemi cognitivi. Il sistema proposto si basa su un controllore che
apprende dall’evoluzione dell’ambiente circostante la strategia
di adattamento ottima. L’apprendimento utilizza un approccio
ispirato ai processi di sviluppo della coscienza nei sistemi biologici.
L’ottimizzazione delle prestazioni dell’antenna intelligente
nell’inseguimento di terminali in movimento sfrutta l’esperienza
maturata dal sistema durante il proprio funzionamento. Nel
contributo `e illustrata l’architettura di un’applicazione basata
sulla strategia proposta e sono mostrati alcuni esempi di
funzionamento dello stesso.
Index Terms—Antenne intelligenti, sistemi cognitivi, intelligenza
artificiale, cognitivit `a embodied
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