On the Performance of Underlay Cognitive Radio Networks Using M/G/1/K Queueing Model

2013 ◽  
Vol 17 (5) ◽  
pp. 876-879 ◽  
Author(s):  
Thi My Chinh Chu ◽  
Hoc Phan ◽  
H. Zepernick
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xu Han ◽  
Lei Xue ◽  
Ying Xu

In the underlay cognitive radio networks (CRNs), the power spectral density (PSD) maps play a foundational role in detecting the idle radio resources. However, it is hard to get a high-accurate PSD map estimation result because of the complicated radio environment. For this reason, we propose a novel convolutional neural network- (CNN-) based PSD map estimation algorithm named map reconstruction CNN (MRCNN). Using the CNN to estimate PSD maps for underlay CRNs has not been reported until now. First, on the basis of the proposed color mapping process, we transform the PSD map estimation task to the image reconstruction task. Then, we train the MRCNN to learn the radio environment characteristics from the training data, rather than making direct biased or imprecise wireless environment hypotheses as in the conventional methods. We utilize the extracted knowledge in the training process to reconstruct the PSD map images. As demonstrated in the simulations, the proposed MRCNN method has a better PSD map estimation performance than the conventional methods.


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