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We consider a multiuser cognitive radio setting where multiple secondary systems, consisting of transmitter-receiver pairs, coexist with multiple primary systems. Each secondary transmitter is equipped with multiple antennas, while all receivers use a single antenna. In this setting, the secondary transmitters are to operate under the constraints of producing restricted amount of interference at the...
This contribution studies the problem of learning sparse, nonparametric models from observations drawn from an arbitrary, unknown distribution. This specific problem leads us to an algorithm extending techniques for Multiple Kernel Learning (MKL), functional ANOVA models and the Component Selection and Smoothing Operator (COSSO). The key element is to use a data-dependent regularization scheme adapting...
We develop and analyze a space time coded cooperative diversity protocol that achieves improved quality of service for mobile users in the downlink of small cells. The protocol, called cooperative frequency reuse (CFR), leverages the cellular frequency reuse concept to create space and frequency diversity among pairs of adjacent base stations. The CFR protocol, which is consistent with the half-duplex...
In this paper, a new algorithm for distributed learning in sensor networks is developed. The algorithm is built upon a diffusion protocol to implement cooperation among neighbouring nodes. The algorithm is developed in the convex set theoretic approach, and it is based on a sequence of metric projections on hyperslabs. Full convergence results have been obtained and the experimental set up demonstrates...
The detection of radioactive contraband is a critical problem in maintaining national security for any country. Emissions from threat materials challenge both detection and measurement technologies especially when concealed by various types of shielding complicating the transport physics significantly. The development of a model-based sequential Bayesian processor that captures both the underlying...
The ever increasing demand on remote sensing capabilities directly conflicts with the accelerating loss of spectrum allocation. Increased spectral awareness and waveform diversity can be applied to this problem through cognitive processing and control of modern radar. This paper motivates the development of essential technology for this purpose.
This paper presents a new frequency allocation scheme for ad hoc networks. Unlike other proposals, the scheme allocates frequencies to groups of nodes, for their intra-group communication needs, in order to guarantee a high level of availability of the needed frequency resources. This scheme uses a distributed decision process with interference measurements and a simple collaboration protocol between...
With all the potential flexibility of software-defined radios, the flexibility of SDR terminals is currently limited to design time flexibility. The capacity of the platform in terms of processing resources and internal bandwidths is dimensioned for the range of supported functionalities. In a platform-independent design scenario, resource managers play the role of matching waveform demands with platform...
In High-Frequency (HF) Over The Horizon (OTH) radar, the space-time variation of the ionospheric channel, external noise as well as transmission channel limitations, is one of the most critical and challenging aspects of the system design and control. Waveforms parameters must be adaptively tuned to the actual external conditions. The purpose of this paper is to define and analyse a technique to set...
We address adaptive detection of Swerling 2 pulse trains by an array of antennas. The disturbance is modeled in terms of a state space model and the ideas of subspace identification are used to come up with a GLRT-based detector. Such detector is compared by Monte Carlo simulation with a Kelly's detector derived assuming that returns are temporally uncorrelated (but spatially correlated) and that...
In this paper, a comparison among different cooperative spectrum sensing approaches is provided. It is assumed that the secondary terminals autonomously perform local spectrum sensing and forward their decision to a fusion center. It combines the received data to obtain the global decision, i.e. the presence or the absence of the primary user in the monitored environment. In particular, three fusion...
The following topics are dealt with: intelligent platform networks; adaptive learning algorithms; cognitive radio; spectrum sensing and management; signal estimation and tracking; Bayesian machine learning; cognitive radar processing; waveform agile intelligent adaptive sensor signal processing; collaborative sensing techniques; learning theory and modelling; and game theoretic tools for cognitive...
Proposed MIMO and hybrid MIMO/phased array (HMPAR) radar systems have the potential for tremendous flexibility in the choice of the transmit beampattern, through the selection of multiple transmitted signals. This paper considers how one might exploit that flexibility in light of prior information or uncertainty in target spatial location, for parameter estimation or tracking applications. We first...
A quaternion valued recursive least squares algorithm for the processing of the generality of quaternion valued random processes (both circular and noncircular) is introduced. This is achieved by extending the widely linear model from the complex domain, and accounting for the specific properties of quaternion algebra. Firstly, the widely linear quaternionic Wiener solution is introduced which uses...
In this paper, we will provide a straightforward classification of some spectrum sensing strategies derived at Eurecom attempting to show the diversity and advantages of these spectrum sensing techniques. Specifically, two low complexity blind sensing algorithms were developed to detect spectrum holes in the primary user's bands: the distribution analysis detector (DAD) and the algebraic detector...
This paper addresses the robust transceiver optimization in multiple-input and multiple-output cognitive radio network, where primary users (PUs) and secondary users (SUs) coexist in the same spectrum band. In the design of cognitive system, the performance degradation perceived by PU should be strictly restricted even with imperfect channel state information (CSI) at cognitive transmitter and receivers...
We propose a compressive sampling (CS) based MVDR spectrum estimator, which estimates the wideband spectrum from the compressed signals with sub-Nyquist-rate sampling. To analyze detection performance, we derive the statistics of the estimated CS MVDR spectrum considering finite samples. We also show that different compression matrices produce different levels of signal leakage and influence the computation...
Cognitive component analysis, defined as an unsupervised learning of features resembling human comprehension, suggests that the sensory structures we perceive might often be modeled by reducing dimensionality and treating objects in space and time as linear mixtures incorporating sparsity and independence. In music as well as language the patterns we come across become part of our mental workspace...
Spectra collected by hyperspectral sensors over samples of the same material are not deterministic quantities. Their inherent spectral variability can be accounted for by making use of suitable statistical models. Within this framework, the Gaussian Mixture Model (GMM) is one of the most widely adopted models for modeling hyperspectral data. Unfortunately, the GMM has been shown not to be sufficiently...
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