Efficient Energy Detection Methods for Spectrum Sensing under Non-Flat Spectral Characteristics
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|Julkaisu||IEEE Journal on Selected Areas in Communications|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2015|
Cognitive radio is an emerging wireless technology that is capable of efficiently coordinating the use of the currently scarce spectrum resources, and spectrum sensing constitutes its most crucial operation. This paper proposes wideband multichannel spectrum sensing methods utilizing fast Fourier transform or filter-bank-based methods for spectrum analysis. Fine-grained spectrum analysis facilitates optimal energy detection in practical scenarios where the transmitted signal, channel frequency response, and/or receiver frequency response do not follow the commonly assumed boxcar model, which typically assumes, among other things, narrow-band communications with flat spectral characteristics. Such sensing schemes can be tuned to the spectral characteristics of the target primary user signals, allowing simultaneous sensing of multiple target primary signals with low additional complexity. This model is also extended to accounting for the specific scenario of detecting a reappearing primary user during secondary transmission, as well as in spectrum sensing scenarios where the frequency range of a primary user is unknown. Novel analytic expressions are derived for the corresponding probability of false alarm and probability of detection in each case, while the useful concept of the area under the receiver operating characteristics curve is additionally introduced as a single scalar metric for evaluating the overall performance of the proposed spectrum sensing algorithms and scenarios. The derived expressions have a rather simple algebraic representation, which renders them convenient to handle both analytically and numerically. The offered results are also extensively validated through comparisons with respective results from computer simulations and are subsequently employed in evaluating each technique analytically, which provides meaningful insights that are anticipated to be useful in future deployments of cognitive radio systems.