Practical Implementation of Spectrum Sensing and Signal Detection for Satellite Broadcasting Systems

2016 ◽  
Vol E99.B (8) ◽  
pp. 1894-1901
Author(s):  
Hiroyuki KAMATA ◽  
Gia Khanh TRAN ◽  
Kei SAKAGUCHI ◽  
Kiyomichi ARAKI
Keyword(s):  
Signal Detection ◽  
Spectrum Sensing ◽  
Practical Implementation ◽  
Satellite Broadcasting

scholarly journals A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yonghua Wang ◽  
Shunchao Zhang ◽  
Yongwei Zhang ◽  
Pin Wan ◽  
Jiangfan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Signal Detection ◽  
Empirical Mode Decomposition ◽  
Spectrum Sensing ◽  
Clustering Algorithm ◽  
Cooperative Spectrum Sensing ◽  
Information Geometry ◽  
Electromagnetic Environment ◽  
Detection Problem ◽  
Mode Decomposition

In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.

A Quick Overview of Different Spectrum Sensing Techniques

2019 ◽  
pp. 52-69
Author(s):  
Pooja Joshi ◽  
Ashish Bagwari ◽  
Ashish Negi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Practical Implementation ◽  
Cooperative Sensing ◽  
Shadow Fading ◽  
Wireless Technology ◽  
Next Generation ◽  
Wireless Technologies ◽  
Advantages And Disadvantages ◽  
Spectrum Holes

The next generation of emerging wireless technology is dealing with spectrum shortage. For appropriate and practical implementation of latest wireless technologies, the sufficient amount of frequency is needed. Cognitive radio (CR) is introduced as a proficient nominee to manage spectral undersupply problem, as it rapidly increases the use of underutilize spectrum via spectrum sensing. This chapter introduces brief start about spectrum holes in addition to spectrum sensing framework. Further, the chapter explains the issues in spectrum sensing and how the cooperative sensing technique is fit to overcome these issues like shadow fading and receiver uncertainty. Consequently, the various non-cooperative sensing techniques are also discussed including their test statics. The advantages and disadvantages of different sensing techniques is exhibited at the end.

Spectrum sensing in satellite cognitive radios: Blind signal detection technique

2016 ◽  
Vol 58 (6) ◽  
pp. 1377-1384 ◽  
Author(s):  
Bilal Muhammad Khan ◽  
Muhammed Mustaqim ◽  
Bilal A. Khawaja ◽  
Syed ShabeehUlHusnain
Keyword(s):  
Signal Detection ◽  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Detection Technique ◽  
Blind Signal
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