Theoretical Analyses of Maximum Cyclic Autocorrelation Selection Based Spectrum Sensing

被引：0
|
作者：
Narieda, Shusuke [1 ]
Cho, Daiki [2 ,5 ]
Ogasawara, Hiromichi [3 ]
Umebayashi, Kenta [2 ]
Fujii, Takeo [4 ]
Naruse, Hiroshi [1 ]
机构：
[1] Mie Univ, Fac Engn, Dept Inform Eng, Tsu, Mie 5148507, Japan
[2] Tokyo Univ Agr & Technol, Dept Elect & Electron Engn, Koganei, Tokyo 1848588, Japan
[3] Akashi Coll, Nat Inst Technol, Dept Gen Studies, Akashi, Hyogo 6748501, Japan
[4] Univ Electrocommun, Adv Wireless & Commun Res Ctr AWCC, Chofu, Tokyo 1828585, Japan
[5] Panasonic Corp, Kadoma, Osaka, Japan
来源：
IEICE TRANSACTIONS ON COMMUNICATIONS | 2020年 / E103B卷 / 12期
关键词：
cognitive radio; maximum cyclic autocorrelation selection based spectrum sensing; signal cyclostationary; COGNITIVE RADIO;
D O I：
10.1587/transcom.2019EBP3175
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.
引用
收藏
页码：1462 / 1469
页数：8
相关论文
共 50 条
  • [21] Spectrum Sensing Based on Signal Sample Autocorrelation in Rayleigh Fading Channel
    Li, Bin
    Chen, Yuebin
    Dong, Guofang
    Feng, Jihua
    2011 7TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING (WICOM), 2011,
  • [22] Blind spectrum sensing using symmetry property of cyclic autocorrelation function: from theory to practice
    Lise Safatly
    Babar Aziz
    Amor Nafkha
    Yves Louet
    Youssef Nasser
    Ali El-Hajj
    Karim Y Kabalan
    EURASIP Journal on Wireless Communications and Networking, 2014
  • [23] Spectrum Sensing Algorithm Based on Autocorrelation Energy in Cognitive Radio Networks
    Ren, Shengwei
    Zhang, Li
    Zhang, Shibing
    FOURTH INTERNATIONAL CONFERENCE ON WIRELESS AND OPTICAL COMMUNICATIONS, 2016, 9902
  • [24] An Unsigned Autocorrelation Based Blind Spectrum Sensing Approach for Cognitive Radio
    Shaikh, Bushra
    Shah, Syed. M. Zafi
    Umrani, Fahim
    2016 INTERNATIONAL CONFERENCE ON OPEN SOURCE SYSTEMS AND TECHNOLOGIES (ICOSST), 2016, : 48 - 53
  • [25] An Improved Spectrum Sensing Method for DTMB System Based on PN Autocorrelation
    Liang, Linfeng
    Wang, Jun
    Song, Jian
    IEICE TRANSACTIONS ON COMMUNICATIONS, 2013, E96B (06) : 1559 - 1565
  • [26] Improved Spectrum Sensing by Utilizing Signal Autocorrelation
    Sharma, Rajesh K.
    Wallace, Jon W.
    2009 IEEE VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-5, 2009, : 430 - 434
  • [27] An Effective Spectrum Sensing Method Based on Maximum Eigenvector
    Du, Ruiyan
    Liu, Fulai
    Wu, Yaping
    MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 4522 - 4525
  • [28] Eigenvalue spectrum sensing based on receiver selection
    Liu, Zhi-Wen
    Zhang, Hang
    Sun, Shao-Fan
    Zhu, De-Sheng
    Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition), 2011, 12 (04): : 317 - 321
  • [29] Performance analysis of undersampled autocorrelation based spectrum sensing for cognitive radio networks
    Ahmed, Farwa
    Chambers, Pat
    2023 34TH IRISH SIGNALS AND SYSTEMS CONFERENCE, ISSC, 2023,
  • [30] Collaborative autocorrelation-based spectrum sensing of OFDM signals in cognitive radios
    Chaudhari, Sachin
    Lunden, Jarmo
    Koivunen, Visa
    2008 42ND ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, VOLS 1-3, 2008, : 191 - 196
12345