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In a conventional cognitive radio (CR) network, only when the primary user's (PU) frequency bands are sensed to be free, secondary users (SUs) can utilize these frequency band resources. Therefore, spectrum sensing (SS) can improve spectrum utilization. Spectrum sharing means that the SUs are allowed to utilize the licensed spectrum bands belonging to the PU to transmit information with PU simultaneously. Spectrum sharing performs well under the conditions that the interference to the PU is assured to be less than a certain threshold. Non-orthogonal multiple access (NOMA) has attracted considerable interests in recent years, which is seen as an important wireless access scheme for the coming 5G wireless communication system. Simultaneous wireless information and power transfer (SWIPT) is proposed as a popular technique to extend the operation duration of power-supply-limited wireless networks. The CR-NOMA is seen as a special form of the power-domain NOMA, wherein the requirements of the SU and PU are strictly met so that excellent system performance can be achieved. In this paper, a joint frame structure is described, wherein SUs first perform SWIPT for spectrum sensing and then transmit information via an overlay and underlay mode. Moreover, the optimization problem to maximize the achievable throughput for the CR network is presented to obtain the optimal sensing slot, while the total transmission power and the minimum rate requirements of the SUs are both constrained. A joint power allocation and sensing time optimizing algorithm based on dichotomy method are proposed to achieve the optimal solution. The simulation results show that there is a maximal throughput via setting an optimal sensing time for the secondary network.

BackgroundDermatological disease significantly affects patient’s health-related quality of life (HrQoL). Skindex is one of the most frequently used dermatology-specific HrQoL measures. Currently no Chinese version of Skindex is available. The aim of this study was to translate and culturally adapt Skindex-29 and Skindex-16 into Chinese, and to evaluate their reliability and validity.MethodsTranslation and cultural adaption were performed following guidelines for cross-cultural adaption of health-related quality of life measures. Subsequently, a cross-sectional study was conducted in which patients with dermatological disease (n = 225) were enrolled. The Chinese version of Skindex-29 and Skindex-16 and Dermatology Life Quality Index (DLQI) were completed. Reliability was evaluated with internal consistency using Cronbach’s alpha. Validity was evaluated using known-groups validity, convergent validity and factor structure validity.ResultsThere were both seven items of Skindex-29 and Skindex-16 requiring a second forward- and backward- translation to achieve the final satisfactory Chinese version. The internal consistency reliability was high (range of Cronbach’s alpha for the scales of Skindex-29 0.85-0.97, Skindex-16 0.86-0.96). Known-group validity was demonstrated by higher scores from patients with inflammatory dermatosis than from patients with isolated skin lesions (P < 0.05). Evidence of factor structure validity of the Skindex-29 and Skindex-16 was demonstrated by both exploratory factor analysis that accounted for 68.66% and 77.78% of the total variance, respectively, and confirmatory factor analysis with acceptable fitness into the expected three-factor structure.ConclusionThis study has developed semantically equivalent translations of Skindex-29 and Skindex-16 into Chinese. The evaluation of the instruments’ psychometric properties shows they have substantial evidence of reliability and validity for use as HrQoL instruments in Chinese patients with dermatological disease.

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With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods.

Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.

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Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.