| As we all know, the wireless spectrum resource is very scarce globally. In order to improve the utilization of the wireless spectrum, the conception of Cognitive Radio (CR) was proposed by J. Mitola in 1999. In CR theory, a cognitive user can first search an unoccupied authorized channel, and then use it for transmitting signals. So obviously, the spectrum detection is one of the key technologies in CR. And as we also know, when it comes to detection for the wideband spectrum, the samplings should be two times of the bandwidth according to the Nyquist's Theorem, which made the hardware hard to burden. Luckily, the appealing of the Compressed Sensing (CS) theory changes the situation. On one hand, the CS theory permits the hardware to deal with the signal far below the Nyquist's Sampling Rate, which can release the pressure on the hardware's processing ability. On the other hand, the radio signals are sparse in the frequency domain, which satisfied one of the preconditions to apply the CS theory on. Thus, the research on the CS-based spectrum sensing technology is available and quite meaningful.The first chapter of the paper introduces the background of our research and the fundamental knowledge of CR. The second chapter of the paper introduces the CS theory detailedly. The third chapter of the paper focuses on the l1 norm minimum algorithm and its improved algorithms. We propose a new improved algorithm, which combines the iterative weighted l1 norm algorithm with the constraints of l1 norm. The simulations show that the improved algorithm works well in the spectrum detection. In the forth and fifth chapter of the paper, our research is focused on the Bayesian CS (BCS) algorithm and an optimized BCS (OBCS) algorithm. Then we simulate them in the spectrum detection scene. At last we take energy detection and BCS spectrum detection which judged by energy as the examples to compare the traditional spectrum detection and the compressed sensing spectrum detection. |
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