Gianmarco Romano

Joint end-to-end loss-delay hidden Markov model for periodic UDP traffic over the Internet

Performance of real-time applications on network communication channels is strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and exhibit a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, an HMM is proposed that shows excellent performance in modeling typical channel behaviors in a set of real packet links. The system is trained with a modified version of the Expectation-Maximization (EM) algorithm. Hidden-state analysis shows how the state variables characterize channel dynamics. State-sequence estimation is obtained by the use of Viterbi algorithm. Real-time modeling of the channel is the first step to implement adaptive communication strategies.

Optimality of Received Energy in Decision Fusion Over Rayleigh Fading Diversity MAC With Non-Identical Sensors

Received-energy test for non-coherent decision fusion over a Rayleigh fading multiple access channel (MAC) without diversity was recently shown to be optimum in the case of conditionally mutually independent and identically distributed (i.i.d.) sensor decisions under specific conditions [C. R. Berger, M. Guerriero, S. Zhou, and P. Willett, “PAC vs. MAC for Decentralized Detection Using Noncoherent Modulation,” IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3562-2575, Sep. 2009], [F. Li, J. S. Evans, and S. Dey, “Decision Fusion Over Noncoherent Fading Multiaccess Channels,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4367-4380, Sep. 2011]. Here, we provide a twofold generalization, allowing sensors to be non identical on one hand and introducing diversity on the other hand. Along with the derivation, we provide also a general tool to verify optimality of the received energy test in scenarios with correlated sensor decisions. Finally, we derive an analytical expression of the effect of the diversity on the large-system performances, under both individual and total power constraints.

Performance Analysis of Time-Reversal MUSIC

In this paper, we study the performance of multiple signal classification (MUSIC) in computational time-reversal (TR) applications. The analysis builds upon classical results on first-order perturbation of singular value decomposition. The closed form of mean-squared error (MSE) matrix of TR-MUSIC is derived for the single-frequency case in both multistatic co-located and non co-located scenarios. The proposed analysis is compared with Cramér-Rao lower-bound (CRLB), and it is exploited for comparison of TR-MUSIC when linear and (nonlinear) multiple-scattering is present. Finally, a numerical analysis is provided to confirm the theoretical findings.

One-Bit Decentralized Detection With a Rao Test for Multisensor Fusion

In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

Performance Analysis and Design of Maximum Ratio Combining in Channel-Aware MIMO Decision Fusion

In this paper we present a theoretical performance analysis of the maximum ratio combining (MRC) rule for channel-aware decision fusion over multiple-input multiple-output (MIMO) channels for (conditionally) dependent and independent local decisions. The system probabilities of false alarm and detection conditioned on the channel realization are derived in closed form and an approximated threshold choice is given. Furthermore, the channel-averaged (CA) performances are evaluated in terms of the CA system probabilities of false alarm and detection and the area under the receiver operating characteristic (ROC) through the closed form of the conditional moment generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC) quadrature rules. Furthermore, we derive the deflection coefficients in closed form, which are used for sensor threshold design. Finally, all the results are confirmed through Monte Carlo simulations.

Orthogonality and Cooperation in Collaborative Spectrum Sensing through MIMO Decision Fusion

This paper deals with spectrum sensing for cognitive radio scenarios where the decision fusion center (DFC) exploits array processing. More specifically, we explore the impact of user cooperation and orthogonal transmissions among secondary users (SUs) on the reporting channel. To this aim four protocols are considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and cooperative. The DFC employs maximum ratio combining (MRC) rule and performance are evaluated in terms of complementary receiver operating characteristic (CROC). Analytical results, coupled with Monte Carlo simulations, are presented.

A hidden Markov model for Internet channels

Performance of real-time applications on network communication channels are strongly related to losses and temporal delays. Several studies have shown that these network features may be correlated and present a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modelled by a hidden Markov model (HMM) with appropriate hidden variables that captures the current state of the network. In this paper we propose an HMM that, trained with a modified version of the EM-algorithm, shows excellent performance in modelling typical channel behaviors in a set of real packet links.

Optimal correlating transform for erasure channels

In this letter, we derive a gradient-based algorithm for computing the optimal transform when coefficients are transmitted over an erasure channel whose statistics are known. The discrete transform introduces correlation among the coefficients with consequent performance improvement against losses. Simulations show appreciable improvements over standard schemes and also good robustness when loss probabilities are only roughly estimated.

A tree-search algorithm for ML decoding in underdetermined MIMO systems

It is well known that Maximum Likelihood (ML) detection for multiantenna and/or multiuser systems has complexity that grows exponentially with the number of antennas and/or users. A number of suboptimal algorithms has been developed in the past that present an acceptable computational complexity and good approximations of the optimal solution. In this paper we propose a tree-search algorithm that provides the exact ML solution with lower computational complexity than that required by an exhaustive search of minimum distance. Also a two-stage tree-search algorithm is presented based on the idea that the ML solution is in the set of equilibrium points of a Hopfield Neural Networks (HNN). The two algorithms work without any modification both in underloaded and overloaded (underdetermined) systems. Numerical simulations show that improvements, in terms of computational complexity measured as the average number of required sum and/or products, are encouraging.

On MSE performance of time-reversal MUSIC

In this paper we study the performance of time-reversal multiple signal classification (TR-MUSIC) for computational TR applications. The analysis builds upon classical results on first-order perturbation of singular value decomposition. The closed form of mean-squared error (MSE) matrix of TR-MUSIC is derived for a narrowband multistatic co-located scenario and is compared with both numerical simulations and the Cramér-Rao lower bound.