In different environments, there are some differences characteristics of the wireless channel. These differences are called "fingerprint". Extracting the "fingerprint" characteristics of different channels in different environment is very important to the development of wireless communication. This paper is focus on the problem of "fingerprint extraction". Through wireless channel signal inversion and K-means clustering and compressed sensing, establishing an adaptive clustering model. And then establish a reasonable "fingerprint" feature with the existing data and relevant physical background. Use MATLAB to solve the model and verify the accuracy. Computer verification and comparison analysis show that this model can b used to identify the wireless channels sith high accuracy.
목차
Abstract 1. Introduction 2. Feature Extraction of Wireless Channel 3. Adaptive Clustering Model Establish 3.1.Wireless Channel Signal Based on Compressed Sensing 3.2 The fitting of ideal signal 3.3. Analysis of the Rationality of Fingerprint Characteristics 3.4. K-Nearest Neighbor (KNN) Classification Model Based on Local Weighted Mean 4. Model Verification 5. Conclusion References
보안공학연구지원센터(IJFGCN) [Science & Engineering Research Support Center, Republic of Korea(IJFGCN)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Future Generation Communication and Networking
간기
격월간
pISSN
2233-7857
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Future Generation Communication and Networking Vol.9 No.11