In traditional cognitive radio (CR) network, most existing graph-based spectrum allocation schemes don't take on-off behavior of primary users (PUs) into consideration. In this paper, a novel spectrum allocation algorithm based on the activities of the PUs is proposed. The proposed algorithm mainly focuses on the vacant probability of licensed spectrums. And it allocates the vacant spectrums considering the interference to the neighbor cognitive nodes and the probability fairness of different cognitive nodes during the allocation. Based on the definition of the obtained benefit of cognitive node, new utility functions are formulated to characterize the system total spectrum utilization and fairness performance from the perspective of available probability. The simulation results validate that the proposed algorithm with low system communication cost is more effective than the traditional schemes when the available licensed spectrums are not sufficient, which is effective and meaningful to a real CR system with bad network condition.
For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.
