Collaborative Spectrum Sensing from Sparse Observations Using Matrix Completion for Cognitive Radio Networks

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure

Relationship between adherence to anti-diabetic medication and depression among patients with diabetes mellitus in three selected Chinese hospitals

Purpose: To determine the relationship between adherence to anti-diabetic medication and depression among patients with diabetes mellitus in three hospitals in Chinese.Methods: This research utilized a quantitative and descriptive design, and included 200 diabetic patients who fulfilled the inclusion criteria for recruitment through a convenient sampling technique. The study applied Beck’s depression inventory II scale for assessment of depression, and a questionnaire for adherence to anti-diabetic medication.Results: A total of 64 (32 %) participants had diabetes for 6 to 10 years. There was a high level of adherence to anti-diabetic medication in 96 patients (48 %); 74 participants (37 %) had moderate adherence to anti-diabetic medication, while 30 patients (15 %) had low adherence. A majority of the patients (181, 90.5 %) had no depression. Six (6) patients (3 %) had mild mood disturbance, 2 patients (1 %) had borderline clinical depression, while 11patients (5.5 %) experienced moderate depression.Adherence to anti-diabetic medication was not associated with depression (p = 0.068). However, depression was associated with age ˃ 50 years (p = 0.041), female sex (p = 0.043), long duration of illness (> 5-years) (p = 0.048), and presence of one or more comorbidities (p = 0.049).Conclusion: There was no association between adherence to anti-diabetic medication and depression among diabetic patients. Keywords: Adherence to anti-diabetic medication, Beck’s depression inventory II, Depression, Diabetes mellitu

The electronic structure of intrinsic magnetic topological insulator MnBi2Te4 quantum wires

The ferromagnetic and antiferromagnetic nanostructure are crucial for fundamental spintronics devices, motivated by its potential application in spintronics, we theoretically investigate the electronic structure of the ferromagnetic and antiferromagnetic phases of the cylindrical intrinsically magnetic topological insulator $\mathrm{MnBi_{2}Te_{4}}$ quantum wires for both cases. We demonstrate that a few surface states exist between the bulk band gap in the ferromagnetic phase, with only one spin branch. In the antiferromagnetic phase, we show that three coexistent states exist between the energy gaps of the quantum wires

Resolution of finite fuzzy relation equations based on strong pseudo-t-norms

AbstractThis work studies the problem of solving a sup-T composite finite fuzzy relation equation, where T is an infinitely distributive strong pseudo-t-norm. A criterion for the equation to have a solution is given. It is proved that if the equation is solvable then its solution set is determined by the greatest solution and a finite number of minimal solutions. A necessary and sufficient condition for the equation to have a unique solution is obtained. Also an algorithm for finding the solution set of the equation is presented