TY - EJOU
AU - Sun, Haoxiang
AU - Chen, Changxing
AU - Ling, Yunfei
AU - Yang, Mu
TI - Cooperative Perception Optimization Based on Self-Checking Machine Learning
T2 - Computers, Materials \& Continua
PY - 2020
VL - 62
IS - 2
SN - 1546-2226
AB - In the process of spectrum perception, in order to realize accurate perception of
the channel state, the method of multi-node cooperative perception can usually be used.
However, the first problem to be considered is how to complete information fusion and
obtain more accurate and reliable judgment results based on multi-node perception results.
The ideas put forward in this paper are as follows: firstly, the perceived results of each node
are obtained on the premise of limiting detection probability and false alarm probability.
Then, on the one hand, the weighted fusion criterion of decision-making weight optimization
of each node is realized based on a genetic algorithm, and the useless nodes also can be
screened out to reduce energy loss; on the other hand, through the linear fitting ability of
RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the
normal operation of the perceptive work of each node. What's more, the real-time training
data can be obtained by spectral segmentation technology to ensure the real-time accuracy of
the optimization results. Finally, the simulation results show that this method can effectively
improve the accuracy and stability of channel perception results, optimize the structure of
the cooperative network and reduce energy consumption.
KW - Spectrum sensing
KW - cooperative sensing
KW - genetic algorithm
KW - neural network
KW - fusion criteria
KW - self-checking
DO - 10.32604/cmc.2020.05625