782 research outputs found
A NLLS based sub-Nyquist rate Spectrum Sensing for Wideband Cognitive Radio
For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a NLLS estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels in a low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate
Wideband and Narrowband Spectrum Sensing Methods Using Software Defined Radios
The ability to accurately sense the surrounding wireless spectrum, without having any prior information about the type of signals present, is an important aspect for dynamic spectrum access and cognitive radio. Energy detection is one viable method, however its performance is limited at low SNR and must adhere to Nyquist sampling theorem. Compressive sensing has emerged as a potential method to recover wideband signals using sub-Nyquist sampling rates, under the presumption that the signals are sparse in a certain domain. In this study, the performance and some of the practical limitations of energy detection and compressive sensing are compared via simulation, and also implementation using the Universal Software Radio Peripheral (USRP) software defined radio (SDR) platform. The usefulness and simplicity of the USRP and GNU Radio software toolkit for simulation and experimentation, as well as some other application areas of compressive sensing and SDR, is also discussed
Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access
Wideband spectrum sensing detects the unused spectrum holes for dynamic
spectrum access (DSA). Too high sampling rate is the main problem. Compressive
sensing (CS) can reconstruct sparse signal with much fewer randomized samples
than Nyquist sampling with high probability. Since survey shows that the
monitored signal is sparse in frequency domain, CS can deal with the sampling
burden. Random samples can be obtained by the analog-to-information converter.
Signal recovery can be formulated as an L0 norm minimization and a linear
measurement fitting constraint. In DSA, the static spectrum allocation of
primary radios means the bounds between different types of primary radios are
known in advance. To incorporate this a priori information, we divide the whole
spectrum into subsections according to the spectrum allocation policy. In the
new optimization model, the minimization of the L2 norm of each subsection is
used to encourage the cluster distribution locally, while the L0 norm of the L2
norms is minimized to give sparse distribution globally. Because the L0/L2
optimization is not convex, an iteratively re-weighted L1/L2 optimization is
proposed to approximate it. Simulations demonstrate the proposed method
outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text
overlap with arXiv:1005.180
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