Pierluigi Salvo Rossi

Internet traffic modeling by means of Hidden Markov Models

In this work, we propose a Hidden Markov Model for Internet traffic sources at packet level, jointly analyzing Inter Packet Time and Packet Size. We give an analytical basis and the mathematical details regarding the model, and we test the flexibility of the proposed modeling approach with real traffic traces related to common Internet services with strong differences in terms of both applications/users and protocol behavior: SMTP, HTTP, a network game, and an instant messaging platform. The presented experimental analysis shows that, even maintaining a simple structure, the model is able to achieve good results in terms of estimation of statistical parameters and synthetic series generation, taking into account marginal distributions, mutual, and temporal dependencies. Moreover we show how, by exploiting such temporal dependencies, the model is able to perform short-term prediction by observing traffic from real sources.

Massive MIMO Channel-Aware Decision Fusion

In this paper, we provide a study of channel-aware decision fusion (DF) over a “virtual” multiple-input multiple-output (MIMO) channel in the large-array regime at the DF center (DFC). The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the DFC. The aim is the development of (widely) linear fusion rules, as opposed to the unsuitable optimum log-likelihood ratio (LLR). The proposed rules can effectively benefit from performance improvement via a large array, differently from existing suboptimal alternatives. Performance evaluation, along with theoretical achievable performance and complexity analysis, is presented. Simulation results are provided to confirm the findings. Analogies and differences with uplink communication in a multiuser (massive) MIMO scenario are underlined.

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.

Massive MIMO for Decentralized Estimation of a Correlated Source

We consider a decentralized multi-sensor estimation problem where L sensor nodes observe noisy versions of a correlated random source vector. The sensors amplify and forward their observations over a fading coherent multiple access channel (MAC) to a fusion center (FC). The FC is equipped with a large array of N antennas and adopts a minimum mean-square error (MMSE) approach for estimating the source. We optimize the amplification factor (or equivalently transmission power) at each sensor node in two different scenarios: a) with the objective of total power minimization subject to mean square error (MSE) of source estimation constraint, and b) with the objective of minimizing MSE subject to total power constraint. For this purpose, based on the well-known favorable propagation condition (when L ≪ N) achieved in massive multiple-input multiple-output (MIMO), we apply an asymptotic approximation on the MSE and use convex optimization techniques to solve for the optimal sensor power allocation in a) and b). In a), we show that the total power consumption at the sensors decays as 1/N, replicating the power savings obtained in massive MIMO mobile communications literature. We also show several extensions of the aforementioned scenarios to the cases where sensor-to-FC fading channels are correlated, and channel coefficients are subject to estimation error. Through numerical studies, we also illustrate the superiority of the proposed optimal power allocation methods over uniform power allocation.

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.

On Energy Detection for MIMO Decision Fusion in Wireless Sensor Networks Over NLOS Fading

We analyze sufficiency and optimality of energy detection (ED) for decision fusion. The considered scenario assumes on-off keying (OOK), statistical channel state information and a fusion center (FC) equipped with multiple antennas. The fading is modeled through zero-mean Gaussian mixtures (GM) to deal with arbitrary non-line-of-sight (NLOS) environments. Theoretical findings are supported by numerical simulations.

Noncolocated Time-Reversal MUSIC: High-SNR Distribution of Null Spectrum

We derive the asymptotic distribution of the null spectrum of the well-known Multiple Signal Classification (MUSIC) in its computational Time-Reversal (TR) form. The result pertains to a single-frequency noncolocated multistatic scenario and several TR-MUSIC variants are investigated here. The analysis builds upon the first-order perturbation of the singular value decomposition and allows a simple characterization of null-spectrum moments (up to the second order). This enables a comparison in terms of spectrums stability. Finally, a numerical analysis is provided to confirm the theoretical findings.

Linear MMSE estimation of time-frequency variant channels for MIMO-OFDM systems

This paper proposes two low-complexity two-dimensional channel estimators for MIMO-OFDM systems derived from a joint time-frequency channel estimator. The estimators exploit both time and frequency correlations of the wireless channel via use of Slepian-basis expansions. The computational saving comes from replacing a two-dimensional Slepian-basis expansion with two serially concatenated one-dimensional Slepian-basis expansions. Performance in terms of normalized mean square error (NMSE) vs. signal-to-noise ratio (SNR) is analyzed via numerical simulations and compared with the original estimator. The analysis of the performance takes into account the impact of both system and channel parameters. The estimators are finally tested when used within the loop of an iterative receiver for MIMO-OFDM systems.

Generalized Rao Test for Decentralized Detection of an Uncooperative Target

We tackle distributed detection of a noncooperative target with a wireless sensor network. When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the distance between the sensor and the (unknown) target positions, embedded in symmetric and unimodal noise. The fusion center receives quantized sensor observations through error-prone binary symmetric channels and is in charge of performing a more-accurate global decision. The resulting problem is a two-sided parameter testing with nuisance parameters (i.e., the target position) present only under the alternative hypothesis. After introducing the generalized likelihood ratio test for the problem, we develop a novel fusion rule corresponding to a generalized Rao test, based on Davies’ framework, to reduce the computational complexity. Also, a rationale for threshold-optimization is proposed and confirmed by simulations. Finally, the aforementioned rules are compared in terms of performance and computational complexity.

HMM-Based Decision Fusion in Wireless Sensor Networks With Noncoherent Multiple Access

We develop a novel decision fusion (DF) approach which exploits time-correlation of the unknown binary source under observation through a wireless sensor network (WSN) reporting local decisions to a fusion center (FC) over interfering Rayleigh fading channels. The system is modeled via a hidden Markov model (HMM): both learning and detection phases are developed. The learning phase is blind, i.e. it requires only a set of observations without knowledge of the corresponding source states. Remarkably, the approach allows the FC to take decisions without knowledge of the local sensor performance. Numerical results confirm the effectiveness of the proposed approach.