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The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

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The classical classifiers are ineffective in dealing with the problem of imbalanced big dataset classification. Resampling the datasets and balancing samples distribution before training the classifier is one of the most popular approaches to resolve this problem. An effective and simple hybrid sampling method based on data partition (HSDP) is proposed in this paper. First, all the data samples are partitioned into different data regions. Then, the data samples in the noise minority samples region are removed and the samples in the boundary minority samples region are selected as oversampling seeds to generate the synthetic samples. Finally, a weighted oversampling process is conducted considering the generation of synthetic samples in the same cluster of the oversampling seed. The weight of each selected minority class sample is computed by the ratio between the proportion of majority class in the neighbors of this selected sample and the sum of all these proportions. Generation of synthetic samples in the same cluster of the oversampling seed guarantees new synthetic samples located inside the minority class area. Experiments conducted on eight datasets show that the proposed method, HSDP, is better than or comparable with the typical sampling methods for F-measure and G-mean.

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