We study channel-aware binary-decision fusion over a shared Rayleigh flat-fading channel with multiple antennas at the Decision Fusion Center (DFC). We present the optimal rule and derive sub-optimal fusion rules, as alternatives with improved numerical stability, reduced complexity and lower system knowledge required. The set of rules is derived following both "Decode-and-Fuse" and "Decode-then-Fuse" approaches. Simulation results for performances are presented both under Neyman-Pearson and Bayesian frameworks. The effect of multiple antennas at the DFC for the presented rules is analyzed, showing corresponding benefits and limitations. Also, the effect on performances as a function of the number of sensors is studied under a total power constraint.

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.

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.

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.

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.

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.