A cognitive radio architecture based on sub-Nyquist sampling

Wieruch, Dennis; Pohl, Volker

doi:10.1109/dyspan.2011.5936249

Cited by 2 publications

(1 citation statement)

References 17 publications

(25 reference statements)

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“…A solution is to segment the spectrum of interest into several sub-bands and down-convert each subband to intermediate frequency (IF) for sampling. Another solution for wide-band spectrum sensing based on sub-Nyquist sampling was proposed in [23]. However, this technique can only be applied when the signals are sparse.…”

Section: System Modelmentioning

confidence: 99%

Wideband Spectrum Sensing and Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios

Bkassiny

Jayaweera

et al. 2012

IEEE Trans. Wireless Commun.

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This paper presents an autonomous cognitive radio (CR) architecture, referred to as the Radiobot. This model goes beyond adaptive radio systems to exploit the main ingredients of cognition which, in this context, are mainly self-learning and self-reconfiguration. Without any prior knowledge of the RF environment, the Radiobot applies a sequence of increasingly sophisticated processing steps to detect and identify the sensed signals. In particular, in this paper, it applies a blind energy detection followed by a cyclostationary detection method to detect the active signals and extract their underlying periodic properties as reflected in cyclic frequencies. These extracted signal features are classified based on the Chinese restaurant process (CRP) and a learning algorithm is applied to achieve autonomous selfreconfiguration of the sensing module. We analyze the impact of fading and Doppler frequency shift on both the energy and cyclostationary detections, and show the receiver operating characteristic (ROC) of the carrier frequency detector. We show the robustness of the cyclostationary detection against channel fading and wide-sense stationary noise. Simulation results are presented to verify the multi-band operability and the reconfiguration ability of the Radiobot and to verify the convergence of the proposed learning algorithm.

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Section: System Modelmentioning

confidence: 99%