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MULTI-COSET SAMPLING FOR POWER SPECTRUM BLIND SENSING
| Content Provider | CiteSeerX |
|---|---|
| Author | Arian, Dyonisius Dony Leus, Geert Zhi Tian, T. |
| Abstract | Power spectrum blind sampling (PSBS) consists of a sam pling procedure and a reconstruction method that is able to recover the unknown power spectrum of a random signal from the obtained sub-Nyquist-rate samples. It differs from spec trum blind sampling (SBS) that aims to recover the spectrum instead of the power spectrum of the signal. In this paper, a PSBS solution is first presented based on a periodic sampling procedure. Then, a multi-coset implementation for this sam pling procedure is developed by solving the so-called minimal sparse ruler problem, and the coprime sampling technique is tailored to fit into the PSBS framework as well. It is shown that the proposed multi-coset implementation based on mini mal sparse rulers offers advantages over coprime sampling in terms of reduced sampling rates, increased flexibility and an extended range of estimated auto-correlation lags. These ben efits arise without putting any sparsity constraint on the power spectrum. Application to sparse power spectrum recovery is also illustrated. Index Terms- Multi-coset sampling, sparse ruler, co prime sampling |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Power Spectrum Multi-coset Implementation Extended Range Index Term Multi-coset Sampling Psbs Solution Spec Trum Power Spectrum Recovery Co Prime Sampling Sub-nyquist-rate Sample Coprime Sampling Technique So-called Minimal Sparse Ruler Problem Random Signal Sparse Ruler Reduced Sampling Rate Sparsity Constraint Auto-correlation Lag Psbs Framework Periodic Sampling Procedure Reconstruction Method Power Spectrum Blind Sampling Ben Efits Unknown Power Spectrum |
| Content Type | Text |
