scholarly journals Low-SNR Asymptotics of Cross-Receiver Mutual Information in Gaussian Channels

Author(s):  
Adam Williamson

<div>In this paper, we analyze the low-SNR behavior of the cross-receiver mutual information (CMI) between two received signals corrupted by uncorrelated, additive Gaussian noise. This framework has use in distributed, passive sensor applications, such as passive radar and collaborative opportunistic navigation. For Gaussian and BPSK signaling, the CMI can be expressed in terms of the effective SNR between the receivers. On-off keying (OOK), while not optimal in terms of spectral efficiency for a single-receiver channel, is shown to have greater CMI than Gaussian or BPSK signaling. This is in spite of the fact that, given the same received SNRs, all three source distributions have the same linear correlation coefficient. This indicates that for OOK sources, effective SNR and correlation coefficient are not meaningful descriptors for passive receivers.<br></div><div><br></div><div>Full-length version of conference paper submission.</div>

2021 ◽  
Author(s):  
Adam Williamson

<div>In this paper, we analyze the low-SNR behavior of the cross-receiver mutual information (CMI) between two received signals corrupted by uncorrelated, additive Gaussian noise. This framework has use in distributed, passive sensor applications, such as passive radar and collaborative opportunistic navigation. For Gaussian and BPSK signaling, the CMI can be expressed in terms of the effective SNR between the receivers. On-off keying (OOK), while not optimal in terms of spectral efficiency for a single-receiver channel, is shown to have greater CMI than Gaussian or BPSK signaling. This is in spite of the fact that, given the same received SNRs, all three source distributions have the same linear correlation coefficient. This indicates that for OOK sources, effective SNR and correlation coefficient are not meaningful descriptors for passive receivers.<br></div><div><br></div><div>Full-length version of conference paper submission.</div>


2008 ◽  
Vol 54 (7) ◽  
pp. 3221-3231 ◽  
Author(s):  
SÉbastien de la Kethulle de Ryhove ◽  
Ninoslav Marina ◽  
Geir E. Oien

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wenzhou Wang ◽  
Limeng Shi ◽  
Xiaoqian Zhu

The dependencies between different business lines of banks have serious effects on the accuracy of operational risk estimation. Furthermore, the dependencies are far more complicated than simple linear correlation. While Pearson correlation coefficient is constructed based on the hypothesis of a linear association, the mutual information that measures all the information of a random variable contained in another random variable is a powerful alternative. Based on mutual information, the generalized correlation coefficient which can capture both linear and nonlinear correlation can be derived. This paper models the correlation between business lines by mutual information and normal copula. The experiment on a real-world Chinese bank operational risk data set shows that using mutual information to model the dependencies between business lines is more reasonable than linear correlation.


2011 ◽  
Vol 57 (11) ◽  
pp. 7307-7312 ◽  
Author(s):  
Yihong Wu ◽  
Dongning Guo ◽  
Sergio Verdu

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