Spectrum Sensing Method Based On Sparse Decomposition

In recent years, with the wide application of new radio technologies, new business and the rapid development of communication technology, the extent and quantity for spectrum resources are increasing soon, so how to use it more efficient in the case of the limited spectrum resources show imminent. The proposition of cognitive radio provides a method for us to solve this problem.Spectrum sensing technology is the basis for cognitive radio, it is important for the performance of system. Sparse decomposition has the ability to seize the main characteristics of the signal and extract the signal components, with noise cancellation function. In recent years, the rise of the compressed sensing technology is also based on the signal can be sparse decomposed in a transform domain. It can break out of the Nyquist sampling theorem, by only a small amount of compression measurements will be able to achieve the reconstructed signal. In this paper, the spectrum sensing method based on sparse decomposition are studied.In the spectral energy detection method, the threshold is directly related to the noise ratio of the received signal. Therefore, noise ratio of the received signal is a decisive factor for spectrum sensing effect. Within a certain range, with lower SNR, detection probability under CFAR decline rapidly. In this paper, considering the sparse decomposition denoising effect, we introduce the sparse decomposition into the receiver front end. After receiving the signal, we remove noise, t hen use it for spectrum sensing. After the sparse decomposition de-noising process, with SNR improving, we set a new threshold in the current SNR, detection performance is bound to improved greatly.In cognitive MIMO system, multi-antenna provides a higher detection reliability, but it also brings sharp increase amount of sample data. The distributed compressed sensing technology is based on a joint sampling of multiple signals, reducing the amount of data samples. It is required to meet the joint sparse model on the basis of multiple signals, while MIMO technology because of the correlation between the antennas, just to meet the JSM-2 model. Therefore, distributed compressed sensing technology based on cognitive MIMO need a joint sparse dictionary in multi-antenna environment. Dictionary training algorithm as a new dictionary acquisition method only gives the case of single-source training signals and how to obtain the training dictionary. In this paper, we combine dictionary training algorithms with joint sparse model under multi-antenna, expand ordinary dictionary training algorithm to multi-antenna joint training dictionary algorithms under three different merger. Compared to the ordinary dictionary training algorithm, the joint dictionary training algorit hm can get better sparse representation effect in the case of the same number of training. Therefore, after the distributed compressed sensing, signal reconstruction probability increased significantly and spectrum sensing performance has also been further improved.