Spectral analysis with incomplete time series: an example from seismology
Computers & Geosciences
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Randomized Signal Processing
Spectrum-blind minimum-rate sampling and reconstruction of multiband signals
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Random sampling estimates of Fourier transforms: antithetical stratified Monte Carlo
IEEE Transactions on Signal Processing
Spectral analysis of nouniformly sampled data: a new approach versus the periodogram
IEEE Transactions on Signal Processing
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Optimal sub-Nyquist nonuniform sampling and reconstruction formultiband signals
IEEE Transactions on Signal Processing
Spectral analysis of randomly sampled signals: suppression of aliasing and sampler jitter
IEEE Transactions on Signal Processing
Random sampling of deterministic signals: statistical analysis of Fourier transform estimates
IEEE Transactions on Signal Processing
Alias-free randomly timed sampling of stochastic processes
IEEE Transactions on Information Theory
Discrete-time spectral estimation of continuous-parameter processes -- A new consistent estimate
IEEE Transactions on Information Theory
Poisson sampling and spectral estimation of continuous-time processes
IEEE Transactions on Information Theory
Alias-free sampling: An alternative conceptualization and its applications
IEEE Transactions on Information Theory
Wideband spectrum sensing technique based on random sampling on grid: Achieving lower sampling rates
Digital Signal Processing
Volume-based method for spectrum sensing
Digital Signal Processing
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A wideband multichannel spectrum sensing approach that utilizes nonuniform sampling and digital alias-free signal processing (DASP) to reliably sense the spectrum using sampling rates well below the ones used in classical DSP is proposed. The approach deploys a periodogram-type spectral analysis tool to estimate the spectrum of the incoming signal from a finite number of its noisy nonuniformly distributed samples. The statistical characteristics of the adopted estimator are analyzed and its accuracy is assessed. It is demonstrated here that owing to the use of nonuniform sampling, the sensing task can be carried out with the use of arbitrary low sampling rates. Most importantly, general guidelines are provided on the required signal analysis time window for a chosen sampling rate to guarantee sensing reliability within a particular scenario. The extra requirement on such recommendations imposed by the presence of noise is given. The analytical results are illustrated by numerical examples. This paper establishes a new framework for multiband spectrum sensing where substantial saving on the used sampling rates can be achieved.