TY - JOUR
T1 - Primary user boundary detection in cognitive radio networks
T2 - Estimated secondary user locations and impact of malicious secondary users
AU - Wang, Huaxia
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - Cognitive radio is an effective technology to improve spectrum utilization through spectrum sharing among primary users (PUs) and secondary users (SUs). Developing accurate and efficient methods to determine the PU coverage is crucial in order to avoid potential interference from SU to PU. In this paper, we propose to use the estimated location of each SU, which is obtained using the self-organizing maps algorithm, to determine the boundary of PU coverage. Furthermore, the PU boundary detection issue considering the presence of malicious SUs is also investigated. Support vector machine (SVM) algorithm, considering both linear and kernel cases, is implemented as the PU boundary detection method. Numerical results show that the boundary detection accuracy based on the estimated network layout approaches the results obtained from an accurate network layout (i.e., each SU with precise location information) and the kernel-based SVM classification results outperform the linear SVM method. In addition, PU boundary detection performance in the presence of malicious SUs is evaluated, in which three different K nearest neighbor based malicious SU detection algorithms are utilized.
AB - Cognitive radio is an effective technology to improve spectrum utilization through spectrum sharing among primary users (PUs) and secondary users (SUs). Developing accurate and efficient methods to determine the PU coverage is crucial in order to avoid potential interference from SU to PU. In this paper, we propose to use the estimated location of each SU, which is obtained using the self-organizing maps algorithm, to determine the boundary of PU coverage. Furthermore, the PU boundary detection issue considering the presence of malicious SUs is also investigated. Support vector machine (SVM) algorithm, considering both linear and kernel cases, is implemented as the PU boundary detection method. Numerical results show that the boundary detection accuracy based on the estimated network layout approaches the results obtained from an accurate network layout (i.e., each SU with precise location information) and the kernel-based SVM classification results outperform the linear SVM method. In addition, PU boundary detection performance in the presence of malicious SUs is evaluated, in which three different K nearest neighbor based malicious SU detection algorithms are utilized.
KW - Cognitive radio
KW - boundary detection
KW - localization
KW - malicious user detection
UR - http://www.scopus.com/inward/record.url?scp=85040931163&partnerID=8YFLogxK
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U2 - 10.1109/TVT.2018.2796024
DO - 10.1109/TVT.2018.2796024
M3 - Article
AN - SCOPUS:85040931163
SN - 0018-9545
VL - 67
SP - 4577
EP - 4588
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
ER -