Error Exponent for Gaussian Channels With Partial Sequential Feedback

2013 ◽  
Vol 59 (8) ◽  
pp. 4757-4766 ◽  
Author(s):  
Manish Agarwal ◽  
Dongning Guo ◽  
Michael L. Honig
Keyword(s):  
Error Exponent ◽  
Gaussian Channels

scholarly journals A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Lei Chen ◽  
Wenquan Feng ◽  
Chuan Lei
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Prior Information ◽  
Finite Size ◽  
Sparse Signals ◽  
Ratio Test ◽  
Sparse Estimation ◽  
Detection Scheme ◽  
Error Exponent ◽  
Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

scholarly journals A Finite Input Alphabet Perspective on the Rate-Energy Tradeoff in SWIPT Over Parallel Gaussian Channels

2019 ◽  
Vol 37 (1) ◽  
pp. 48-60 ◽  
Author(s):  
Rakshith Rajashekar ◽  
Marco Di Renzo ◽  
Lie-Liang Yang ◽  
K.V.S. Hari ◽  
Lajos Hanzo
Keyword(s):  
Gaussian Channels ◽  
Input Alphabet
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