| Title | A GUI for Wideband Spectrum Sensing using Compressive Sampling Approaches |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Chandrala, M S, Hadli, Pooja, Aishwarya, R, Jejo, Kevin C, Sunil, Y, Sure, Pallaviram |
| Conference Name | 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) |
| Keywords | Bayes methods, Cognitive radio, cognitive radio networks, composability, compressed sensing, compressive sampling, compressive sampling approaches, Cyber-physical systems, device communications, effective spectral resource utilization, formidable owing, graphical user interface, graphical user interface (GUI), graphical user interfaces, GUI, Iterative methods, Matching pursuit algorithms, measurement vectors, modulated wideband converter, Modulated Wideband Converter (MWC), multiple measurement vector framework, MWC, newer spectral opportunities, Nyquist sampling rates, OMP, orthogonal matching pursuit, orthogonal matching pursuit (OMP), privacy, prominent solution, pubcrawl, radio networks, radio spectrum management, rapidly growing device, Resiliency, SBL approach, Sensors, signal detection, signal reconstruction, signal sampling, Signal to noise ratio, Sparse Bayesian Learning (SBL), Sparse Bayesian Learning algorithms, spectral support, support recovery, Wideband, wideband spectrum sensing |
| Abstract | Cognitive Radio is a prominent solution for effective spectral resource utilization. The rapidly growing device to device (D2D) communications and the next generation networks urge the cognitive radio networks to facilitate wideband spectrum sensing in order to assure newer spectral opportunities. As Nyquist sampling rates are formidable owing to complexity and cost of the ADCs, compressive sampling approaches are becoming increasingly popular. One such approach exploited in this paper is the Modulated Wideband Converter (MWC) to recover the spectral support. On the multiple measurement vector (MMV) framework provided by the MWC, threshold based Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) algorithms are employed for support recovery. We develop a Graphical User Interface (GUI) that assists a beginner to simulate the RF front-end of a MWC and thereby enables the user to explore support recovery as a function of Signal to Noise Ratio (SNR), number of measurement vectors and threshold. The GUI enables the user to explore spectrum sensing in DVB-T, 3G and 4G bands and recovers the support using OMP or SBL approach. The results show that the performance of SBL is better than that of OMP at a lower SNR values. |
| DOI | 10.1109/ICCCNT45670.2019.8944766 |
| Citation Key | chandrala_gui_2019 |
A GUI for Wideband Spectrum Sensing using Compressive Sampling Approaches