Nearest neighbor decoding for additive non-Gaussian noise channels

1996 ◽  
Vol 42 (5) ◽  
pp. 1520-1529 ◽  
Author(s):  
A. Lapidoth
Keyword(s):  
Gaussian Noise ◽  
Nearest Neighbor ◽  
Non Gaussian ◽  
Gaussian Noise Channels

scholarly journals Multicarrier Downlink Transmission for High-speed Railway in Non-Gaussian Noise Channels

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52607-52615 ◽  
Author(s):  
Qiwei Zheng ◽  
Fanggang Wang ◽  
Bo Ai ◽  
Zhangdui Zhong
Keyword(s):  
Gaussian Noise ◽  
High Speed ◽  
High Speed Railway ◽  
Downlink Transmission ◽  
Non Gaussian ◽  
Gaussian Noise Channels

Extension of de Bruijn's identity to dependent non-Gaussian noise channels

2016 ◽  
Vol 53 (2) ◽  
pp. 360-368 ◽  
Author(s):  
Nayereh Bagheri Khoolenjani ◽  
Mohammad Hossein Alamatsaz
Keyword(s):  
Gaussian Noise ◽  
Additive Noise ◽  
Differential Entropy ◽  
Conditional Mean ◽  
First Derivative ◽  
Dependent Signal ◽  
The Common ◽  
Non Gaussian ◽  
Gaussian Noise Channels ◽  
Special Case

Abstract De Bruijn's identity relates two important concepts in information theory: Fisher information and differential entropy. Unlike the common practice in the literature, in this paper we consider general additive non-Gaussian noise channels where more realistically, the input signal and additive noise are not independently distributed. It is shown that, for general dependent signal and noise, the first derivative of the differential entropy is directly related to the conditional mean estimate of the input. Then, by using Gaussian and Farlie–Gumbel–Morgenstern copulas, special versions of the result are given in the respective case of additive normally distributed noise. The previous result on independent Gaussian noise channels is included as a special case. Illustrative examples are also provided.

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