Abstract
Spectrum sensing in cognitive radio network determines whether the channel is currently in use or not by the primary user (PU). If the channel is detected to be free, it may be allocated to a secondary user (SU) for certain amount of time. Energy detection spectrum sensing is one simplest and most popular algorithm for detecting PU, but is limited by poor performance at low signal-to-noise ratio (SNR). Frequency domain entropy-based spectrum sensing counteracts the effect of noise uncertainty at low SNR and is robust against variations in noise power. In this article, a real-time spectrum sensing prototype is developed using Raspberry Pi single-board computer and RTL-SDR. GNU Radio models were developed for spectrum sensing for both entropy-based and energy detection spectrum sensing. Finally, performance of entropy-based spectrum sensing is compared with energy detection spectrum sensing using this real-time hardware setup.
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