Spectral analysis with incomplete time series: an example from seismology
Computers & Geosciences
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
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
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
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Alias-free randomly timed sampling of stochastic processes
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
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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This paper presents a method that deploys nonuniform sampling and appropriates to it a processing algorithm to monitor the activity of a number of non-overlapping spectral bands. The proposed approach facilitates the use of sampling rates well below the ones demanded by uniform-sampling-based DSP. Randomized sampling scheme, namely random sampling on grid, in conjunction with a periodogram-type spectral analysis tool is utilized to accomplish the task. The statistical characteristics of the endorsed analysis tool are examined for a finite set of nonuniformly distributed signal samples contaminated with noise. General guidelines are provided to ensure the reliability of the adopted sensing technique where it is affirmed that the sampling rates can be arbitrarily low. The additional requirements on such recommendations imposed by the presence of noise are given. It is demonstrated that in certain scenarios the proposed technique can considerably reduce the complexity of the spectrum sensing procedure. The presented analytical results are illustrated by numerical examples. This paper establishes a new framework for efficient spectrum sensing methods that exploit randomized sampling schemes. Unlike a number of similar approaches in the literature, it offers solutions that are well suited for practical implementation in hardware.