Online Wideband Spectrum Sensing Using Sparsity
Lampros Flokas, Petros Maragos

TL;DR
This paper introduces two online LMS-based algorithms for wideband spectrum sensing that exploit sparsity to enable real-time, low-complexity spectrum estimation in cognitive radio systems, improving efficiency over batch methods.
Contribution
The paper proposes novel online LMS algorithms that enforce sparsity with known or estimated non-zero spectrum components, enhancing real-time spectrum sensing capabilities.
Findings
Algorithms effectively enforce sparsity in spectrum estimates.
Estimated non-zero components improve accuracy.
Compared algorithms show competitive performance.
Abstract
Wideband spectrum sensing is an essential part of cognitive radio systems. Exact spectrum estimation is usually inefficient as it requires sampling rates at or above the Nyquist rate. Using prior information on the structure of the signal could allow near exact reconstruction at much lower sampling rates. Sparsity of the sampled signal in the frequency domain is one of the popular priors studied for cognitive radio applications. Reconstruction of signals under sparsity assumptions has been studied rigorously by researchers in the field of Compressed Sensing (CS). CS algorithms that operate on batches of samples are known to be robust but can be computationally costly, making them unsuitable for cheap low power cognitive radio devices that require spectrum sensing in real time. On the other hand, on line algorithms that are based on variations of the Least Mean Squares (LMS) algorithm…
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