[논문]Broadband Spectrum Sensing of Distributed Modulated Wideband Converter Based on Markov Random Field

Li, Zhi; Zhu, Jiawei; Xu, Ziyong; Hua, Wei

doi:10.4218/etrij.2017-0028

Broadband Spectrum Sensing of Distributed Modulated Wideband Converter Based on Markov Random Field 원문보기

ETRI journal, v.40 no.2, 2018년, pp.237 - 245

Li, Zhi (College of Electronics and Information Engineering, Sichuan University) , Zhu, Jiawei (College of Electronics and Information Engineering, Sichuan University) , Xu, Ziyong (College of Electronics and Information Engineering, Sichuan University) , Hua, Wei (College of Electronics and Information Engineering, Sichuan University)

Abstract ▼ AI-Helper

The Distributed Modulated Wideband Converter (DMWC) is a networking system developed from the Modulated Wideband Converter, which converts all sampling channels into sensing nodes with number variables to implement signal undersampling. When the number of sparse subbands changes, the number of nodes can be adjusted flexibly to improve the reconstruction rate. Owing to the different attenuations of distributed nodes in different locations, it is worthwhile to find out how to select the optimal sensing node as the sampling channel. This paper proposes the spectrum sensing of DMWC based on a Markov random field (MRF) to select the ideal node, which is compared to the image edge segmentation. The attenuation of the candidate nodes is estimated based on the attenuation of the neighboring nodes that have participated in the DMWC system. Theoretical analysis and numerical simulations show that neighboring attenuation plays an important role in determining the node selection, and selecting the node using MRF can avoid serious transmission attenuation. Furthermore, DMWC can greatly improve recovery performance by using a Markov random field compared with random selection.

주제어

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

가설 설정

In this paper, we proposed the spectrum sensing of a DMWC based on a Markov Random Field. The method of the Markov random field commonly used in image segmentation was introduced first to realize the node selection when the DMWC increases the number of nodes to improve the performance.

제안 방법

In order to verify the desirable effect of the Markov random field on DMWC node selection, we compared the reconstruction rate using two methods: selection in random and the Markov random field. The simulation signal model is given by
In this paper, we propose the spectrum sensing of a DMWC based on a Markov random field (MRF) to select the optimal node. Facing the time-dependent support set problem, the DMWC needs to adjust the number of virtual sampling channels by adding the sensing nodes.
However, different nodes have different attenuation coefficients. The following experiment examines the influence of different degrees of node attenuation on DMWC signal reconstruction in different SNR regimes. The number of sensing nodes in the simulations is 50.
The method of the Markov random field commonly used in image segmentation was introduced first to realize the node selection when the DMWC increases the number of nodes to improve the performance. We analyzed the selection problem in a first-order neighborhood system, and constructed a suitable energy function using a self-weight coefficient and the cross-weight coefficient. In addition, we explored the influence of the distance and transmission attenuation coefficient on the selection probability.

데이터처리

When considering whether the center node is chosen to join the DMWC, it is necessary to analyze the influence of the self-weight coefficient and cross-weight coefficient on the selection probability. Therefore, since we are only concerned with the state of the center node x_i = 1, we calculate the node selected probability as follows:

성능/효과

In addition, we explored the influence of the distance and transmission attenuation coefficient on the selection probability. Finally, numerical simulations indicated that the DMWC could improve the reconstruction rate and the anti-noisy performance by using a Markov random field compared with random selection. However, this work only considers a first-order neighborhood system.

참고문헌 (16)

E. Axell, G. Leus, E.G. Larsson, and H.V. Poor, "Spectrum Sensing for Cognitive Radio: State-of-the-Art and Recent Advances," IEEE Signal Process Mag., vol. 29, no. 3, 2012, pp. 101-116.

상세보기
R. Jayaprakash and K. Visa, "Cooperative Game-Theoretic Approach to Spectrum Sharing in Cognitive Radios," J. Signal Process., vol. 106, Jan. 2015, pp. 15-29.

상세보기
M. Moshe and E. Yonina, "From Theory to Practice: Sub- Nyquist Sampling of Sparse Wideband Analog Signals," JSTSP, vol. 4, no. 2, Apr. 2010, pp. 375-391.
M. Moshe and E. Yonina, "Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals," IEEE Trans. Signal Process., vol. 57, no. 3, Mar. 2009, pp. 993-1009.

상세보기
M. Moshe and E. Yonina, "Wideband Spectrum Sensing at Sub-Nyquist Rates," IEEE Signal Process Mag., vol. 28, no. 4, 2011, pp. 102-135.

상세보기
E. Candes and T. Tao, "Near Optimal Signal Recovery from Random Projections: Universal Encoding Strategies," IEEE Trans. Inform. Theory, vol. 52, no. 12, Dec. 2006, pp. 5406-5425.

상세보기
X. Zhang, L. Xiaozhu, S. Hooman, and B. Jalaian, "Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks," Int. J. Distrib. Sensor Netw., vol. 11, no. 8, Jan. 2015, pp. 1-15.
C. Song and Q. Zhang, "Cooperative Spectrum Sensing with Multi-Channel Coordination in Cognitive Radio Networks," IEEE Int. Conf. Commun., May 23-27, 2010.
X. Ziyong, L. Zhi, and L. Jian, "Broadband Cooperative Spectrum Sensing Based on Distributed Modulated Wideband Converter," Sensors, vol. 16, no. 10, Oct. 2016, pp. 1-12.

상세보기
C. Wei, X. Zhang, J. Liu, and Y. Guan, "Image Segmentation Algorithm Based on Markov Random Field (MRF) for Radiography," High Power Laser Particle Beams, vol. 28, no. 12, Dec. 2016, pp. 124001-1-124001-5.
W. Lei and H. Chenxue, "Improved Hierarchical Markov Random Field Algorithm Color Image Segmentation Algorithm," J. Comput. Appl., vol. 36, no. 9, Sept. 2016, pp. 2579-2579.
Y. Zhou, Z. Zhou, and B. Li, "Sensing Nodes Selection and Data Fusion in Cooperative Spectrum Sensing," IET Commun., vol. 8, no. 13, Sept. 2014, pp. 2308-2314.

상세보기
D. Lee, "Adaptive Random Access for Cooperative Spectrum Sensing in Cognitive Radio Networks," IEEE Trans. Wireless Commun., vol. 14, no. 2, Feb. 2015, pp. 831-840.

상세보기
Y. Peng, F. Al-Hazemi, H. Kim, and C. Youn, "Joint Selection for Cooperative Spectrum Sensing in Wireless Sensor Network," IEEE Sensor J., vol. 16, no. 22, Nov. 2016, pp. 7837-7838.

상세보기
T. Wang, M. Yang, and J. Wu, "Distributed Detection of Dynamic Event Regions in Sensor Networks with a Gibbs Field Distribution and Gaussian Corrupted Measurements," IEEE Trans. Commun., vol. 64, no. 9, Sept. 2016, pp. 3932-3945.

상세보기
R. Versteegen, G. Gimel'farb, and P. Riddle, "Texture Modeling with Nested High-Order Markov-Gibbs Random Fields," Comput. Vis. Imag. Understand., vol. 143, Feb. 2016, pp. 120-134.

상세보기

원문 URL 링크

DOI : 10.4218/etrij.2017-0028 [무료]
ScienceCentral : 저널
Korea Open Access Journals : 저널
John Wiley & Sons, Ltd. : 저널
AccessON : 저널

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

내보내기 메뉴

내보내기 구분

파일저장
인쇄
메일전송

구성항목

기본정보
상세정보

관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관

저장형식

Text(ASCII format)
Excel format
RefWorks Direct Export
RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley

메일정보

받는사람 (필수): @
보내는사람 (선택): @
제목
내용: KISTI 검색결과 이메일 서비스

안내

총 건의 자료가 검색되었습니다.

다운받으실 자료의 인덱스를 입력하세요. (1-10,000)

검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다.

데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요)

다운로드 파일은 UTF-8 형태로 저장됩니다.
파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오.

Text(ASCII format)
Excel format

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

AI-Helper ※ AI-Helper는 을 사용합니다.

AI-Helper

안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

연합인증