Research on the threshold selection of BigBand sparse spectrum sensing algorithm

Home > Archive>Volume 40, Issue 7, 2017 >141-154

Research on the threshold selection of BigBand sparse spectrum sensing algorithm
DOI:
                        
                    
CSTR:
                        [cstr]
                    
Author:
                        Fan KaiFan Kai
1. School of Communication and Information Engineering, Shanghai University, Shanghai 200072,China； 2.Shanghai Institute of Microsystem and Information Technology in Chinese Academy of Science, Shanghai 200050, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Jin Yanliang1Jin Yanliang
School of Communication and Information Engineering, Shanghai University, Shanghai 200072,China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Xiong Yong2Xiong Yong
Shanghai Institute of Microsystem and Information Technology in Chinese Academy of Science, Shanghai 200050, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:1. School of Communication and Information Engineering, Shanghai University, Shanghai 200072,China; 2.Shanghai Institute of Microsystem and Information Technology in Chinese Academy of Science, Shanghai 200050, China
Clc Number:TN911.6
Fund Project:

Article

Figures

Metrics

Reference

Cited by

Materials

Comments

Abstract:

According to the Nyquist sampling theory, it is necessary to have ADCs of capability of GHz sampling speed to sense the spectrumof GHz bandwidth in real time. But this is not possible with the limitations of budget and energy consumption. The BigBand sparse spectrum sensing algorithm, targeting to the sparsity of signals, is capable of sensing 0.9 GHz wide spectrumusing several 50 MHz low speed ADCs. In this paper, the threshold for eliminating out the noise is researched, by using the statistical parameters of noise. A threshold calculating method is proposed, supporting the implements of BigBand algorithm in practices.

Key words:sparse spectrum sensing; BigBand algorithm; sparse Fourier transform

Get Citation

Copy

Article Metrics

Abstract:1387
PDF: 1431
HTML: 0
Cited by: 0

History

Received:
Revised:
Adopted:
Online: August 15,2017
Published:

Home

Introduction

Editorial Committee

Policy

Contact Us

中文版

Get Citation

Share

Article Metrics

History

Article QR Code