Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology

Successive Integer-Forcing and its Sum-Rate Optimality

Integer-forcing receivers generalize traditional linear receivers for the multiple-input multiple-output channel by decoding integer-linear combinations of the transmitted streams, rather then the streams themselves. Previous works have shown that the additional degree of freedom in choosing the integer coefficients enables this receiver to approach the performance of maximum-likelihood decoding in various scenarios. Nonetheless, even for the optimal choice of integer coefficients, the additive noise at the equalizer's output is still correlated. In this work we study a variant of integer-forcing, termed successive integer-forcing, that exploits these noise correlations to improve performance. This scheme is the integer-forcing counterpart of successive interference cancellation for traditional linear receivers. Similarly to the latter, we show that successive integer-forcing is capacity achieving when it is possible to optimize the rate allocation to the different streams. In comparison to standard successive interference cancellation receivers, the successive integer-forcing receiver offers more possibilities for capacity achieving rate tuples, and in particular, ones that are more balanced.Comment: A shorter version was submitted to the 51st Allerton Conferenc

Target DoA estimation in passive radar using non-uniform linear arrays and multiple frequency channels

In this paper we present a robust approach for target direction of arrival (DoA) estimation in passive radar that jointly exploits spatial and frequency diversity. Specifically we refer to a DVB-T based passive radar receiver equipped with a linear array of few antenna elements non-uniformly spaced in the horizontal dimension, able to collect multiple DVB-T channels simultaneously. We resort to a maximum likelihood (ML) approach to jointly exploit the target echoes collected across the antenna elements at multiple carrier frequencies. Along with an expected improvement in terms of DoA estimation accuracy, we show that the available spatial and frequency diversity can be fruitfully exploited to extend the unambiguous angular sector useful for DoA estimation, which represent an invaluable tool in many applications. To this purpose, a performance analysis is reported against experimental data collected by a multi-channel DVB-T based passive radar developed by Leonardo S.p.A