Wi-Fi indoor positioning system has received increasing interest in pervasive computing applications due to its low cost and satisfactory accuracy. To obtain high positioning accuracy based on source limited devices, various AP selection strategies have been proposed to select the most discriminant APs for positioning. In this paper, we propose a spatially localized AP selection method based on joint location information gain. In contrast to traditional AP selection methods which measure the discriminant ability of APs independently, we consider choosing APs jointly. By considering the correlation of the discriminant ability between different APs, more accurate measure of the discriminant ability of APs can be taken. Furthermore, since the optimal AP selection solution varies spatially, we incorporate a location clustering method to localize AP selection and subsequent positioning process. Finally, support vector regression (SVR) algorithm is combined to estimate the location. Experiments are carried in a realistic Wi-Fi indoor environment. Experimental results show that, by using the localized joint AP selection strategy, the proposed positioning method achieves a high-level accuracy while reducing the energy consumption on client devices significantly.
Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.
The spectrum sharing problem between primary and cognitive users is mainly investigated. Since the interference for primary users and the total power for cognitive users are constrained, based on the well-known water-filling theorem, a novel one-user water-filling algorithm is proposed, and then the corresponding simulation results are given to analyze the feasibility and validity. After that this algorithm is used to solve the communication utility optimization problem subject to the power constraints in cognitive radio network. First, through the gain to noise ratio for cognitive users, a subcarrier and power allocation algorithm based on the optimal frequency partition is proposed for two cognitive users. Then the spectrum sharing algorithm is extended to multiuser conditions such that the greedy and parallel algorithms are proposed for spectrum sharing. Theory and simulation analysis show that the subcarrier and power allocation algorithms can not only protect the primary users but also effectively solve the spectrum and power allocation problem for cognitive users.
