scholarly journals Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yulin Wang ◽  
Gengxin Zhang
Keyword(s):  
Fourier Transform ◽  
Discrete Cosine Transform ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Compressive Sampling ◽  
Cosine Transform ◽  
Wideband Spectrum Sensing ◽  
Communication Signal ◽  
Dct Domain ◽  
Sparsity Structure

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity.

scholarly journals A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2053
Author(s):  
Jiayu Wang ◽  
Hongquan Wang ◽  
Xinshan Zhu ◽  
Pengwei Zhou
Keyword(s):  
Discrete Cosine Transform ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Cosine Transform ◽  
Spatial Image ◽  
Multi Scale ◽  
Dct Domain ◽  
High Dynamic ◽  
Fully Connected

Although high dynamic range (HDR) is now a common format of digital images, limited work has been done for HDR source forensics. This paper presents a method based on a convolutional neural network (CNN) to detect the source of HDR images, which is built in the discrete cosine transform (DCT) domain. Specifically, the input spatial image is converted into DCT domain with discrete cosine transform. Then, an adaptive multi-scale convolutional (AMSC) layer extracts features related to HDR source forensics from different scales. The features extracted by AMSC are further processed by two convolutional layers with pooling and batch normalization operations. Finally, classification is conducted by a fully connected layer with Softmax function. Experimental results indicate that the proposed DCT-CNN outperforms the state-of-the-art schemes, especially in accuracy, robustness, and adaptability.

scholarly journals Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio

2019 ◽  
Vol 11 (11) ◽  
pp. 244
Author(s):  
Wang ◽  
Wu ◽  
Yao ◽  
Qin
Keyword(s):  
Fourier Transform ◽  
Spectrum Sensing ◽  
Filter Bank ◽  
Energy Detection ◽  
High Rate ◽  
Idle State ◽  
Trade Off ◽  
Wideband Spectrum Sensing ◽  
Wideband Signals ◽  
Sensing Time

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.

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