TY - GEN
T1 - MAC protocol classification in a cognitive radio network
AU - Yang, Zhuo
AU - Yao, Yu Dong
AU - Chen, Sheng
AU - He, Haibo
AU - Zheng, Di
PY - 2010
Y1 - 2010
N2 - Most media access control (MAC) protocols can be classified as contention based or controlled based according to their transmission mechanisms. To classify contention based or control based MAC protocols in an unknown primary network, we choose received power mean and variance as two features for support vector machines (SVMs) in a machine learning based algorithm. The data consisting of these two features are collected from two primary network models based on time division multiple access (control based) and slotted Aloha (contention based), respectively. In the training process, data along with their identification class labels (say, 1 denotes time division multiple access, and -1 stands for slotted Aloha) are used to train the SVMs. After training, contention or control based MAC protocols can be effectively determined by the trained SVMs embedded in a cognitive radio terminal of a secondary network.
AB - Most media access control (MAC) protocols can be classified as contention based or controlled based according to their transmission mechanisms. To classify contention based or control based MAC protocols in an unknown primary network, we choose received power mean and variance as two features for support vector machines (SVMs) in a machine learning based algorithm. The data consisting of these two features are collected from two primary network models based on time division multiple access (control based) and slotted Aloha (contention based), respectively. In the training process, data along with their identification class labels (say, 1 denotes time division multiple access, and -1 stands for slotted Aloha) are used to train the SVMs. After training, contention or control based MAC protocols can be effectively determined by the trained SVMs embedded in a cognitive radio terminal of a secondary network.
KW - Capture effect
KW - MAC protocol classification
KW - Machine learning
KW - Rayleigh fading
UR - http://www.scopus.com/inward/record.url?scp=77955618040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955618040&partnerID=8YFLogxK
U2 - 10.1109/WOCC.2010.5510617
DO - 10.1109/WOCC.2010.5510617
M3 - Conference contribution
AN - SCOPUS:77955618040
SN - 9781424475964
T3 - WOCC2010 Technical Program - The 19th Annual Wireless and Optical Communications Conference: Converging Communications Around the Pacific
BT - WOCC2010 Technical Program - The 19th Annual Wireless and Optical Communications Conference
T2 - 19th Annual Wireless and Optical Communications Conference, WOCC2010: Converging Communications Around the Pacific
Y2 - 14 May 2010 through 15 May 2010
ER -