A new support identification technique based on factor graphs and belief propagation is proposed for Compressive Sensing (CS) aided Wireless Sensor Networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an Signal to Noise Ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the Orthogonal Matching Pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the Fusion Center (FC), while maintaining highaccuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.

Abstract-We propose a method for blind identification of finite impulse response (FIR) channels with periodic modulation. The time-domain formulation in terms of block signals is simple compared with existing frequency-domain formulations. It is shown that the linear equations relating the products of channel coefficients and the autocorrelation matrix of the received signal can be further arranged into decoupled groups. The arrangement reduces computations and improves accuracy of the solution; it also leads to very simple identifiability conditions and a very natural formulation of the optimal modulating sequence selection problem. The proposed optimal selection minimizes the effects of channel noise and error in autocorrelation matrix estimation; it results in a consistent channel estimate when the channel noise is white. Simulation results show that the method yields good performance: It compares favorably with an existing subspace modulation-induced-cyclostationarity method, and it is robust with respect to channel order overestimation. The effect of modulation period and threshold of the modulating sequence are also discussed.

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