Photonics-assisted compressive sampling system for wideband spectrum sensing (Invited Paper)

2017 ◽  
Vol 15 (1) ◽  
pp. 010012-10017 ◽  
Author(s):  
Qiang Guo Qiang Guo ◽  
Minghua Chen Minghua Chen ◽  
Yunhua Liang Yunhua Liang ◽  
Hongwei Chen Hongwei Chen ◽  
Sigang Yang Sigang Yang ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Compressive Sampling ◽  
Sampling System ◽  
Wideband Spectrum Sensing

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 Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

2021 ◽  
Author(s):  
Xue Wang ◽  
Qian Chen ◽  
Min Jia ◽  
Xuemai Gu
Keyword(s):  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Detection Algorithm ◽  
Channel Structure ◽  
System Structure ◽  
Sensing Matrix ◽  
Sampling System ◽  
Wideband Spectrum Sensing

Abstract As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm, which is based on the advanced sub-Nyquist sampling framework, has the similar performance as MWC and even higher than MWC in some cases.

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