Yi Xu

Cooperative Spectrum Sensing in Cognitive Radio under Noise Uncertainty

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio (CR). However, its performance may suffer from the noise uncertainty, which is portrayed by the SNR wall in some literatures. In this paper we analyze the spectrum sensing performance under noise uncertainty and propose a new approach to obtain the SNR wall. In addition, a suboptimal liner cooperative sensing algorithm with wavelet denoising is proposed to reduce the impact of noise uncertainty. Analysis and numberical results show that cooperative sensing and wavelet denoising can significantly improve the sensing performance under noise uncertainty condition.

Cooperative Spectrum Sensing with Wavelet Denoising in Cognitive Radio

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio (CR). However, few studies make mention of wavelet transform based signal processing before energy detection. In this paper, a novel cooperative energy spectrum sensing algorithm with 1-D or 2-D wavelet denoising is proposed. The 1-D wavelet denoising is performed at each sensing node, while the 2-D wavelet denoisng is implemented at the sensing station. Simulation results show that the spectrum sensing performance is improved significantly by the proposed cooperative spectrum sensing algorithm with wavelet denoising as opposed to the conventional method.

New Half-Voting Cooperative Sensing Algorithms in Cognitive Radio

In cognitive radio (CR) networks, hard fusion is widely applied for cooperative energy spectrum sensing, since it requires only one bit to transmit the decision results between sensing nodes and the sensing station. And half-voting is an effective algorithm in hard fusion. In this paper, two half-voting algorithms are proposed to enhance the sensing performance. In the first half-voting algorithm, we adopt linear data fusion with weights based on the SNR of each sensing node. In another algorithm, when the sensing station has no knowledge of each sensing node’s SNR, the history decisions are utilized to estimate the weight factors. Analyses and numerical results show that the proposed new half-voting algorithms can significantly improve the sensing performance.