Most spectrum sensing algorithms mainly use the characteristics of frequency, time, and geographical dimensions to detect spectrum holes. In this paper, we propose a novel spectrum sensing scheme from the space domain by using beamspace transformation and the support vector machine technology. First, a model of beamspace transformation is proposed for the case of complex calculations in a sizeable multi-antenna system. This beamspace transformation has the ability of spatial filtering, which can not only decrease the dimension of the receive matrix but also enhance the signal to noise ratio of the received signal. Then, we employ the support vector machine classification to overcome the problems caused by the inherent threshold of traditional sensing algorithms. We only need to train the historical samples to distinguish between noise and primary user signals effectively. This classification algorithm has self-learning ability, which can adaptively adjust the classification hyperplane according to environmental changes without complex threshold calculation. Finally, simulation results show that the proposed scheme outperforms other related multi-antenna sensing algorithms, especially under low signal to noise ratio and low snapshot.
With the diversification of the kinds of wireless transmission service, the transmission rate and reliability of wireless communication have been demanded sharply. The shortage of resources of spectrum has become continuously prominent. These are bottlenecks to restrict the development of wireless communication. The current multi-antenna cooperative relay communication system can't sense complex communication scenes agilely, and adjust the strategy of interference suppression according to the signal characteristics adaptively, and optimize the resource of the system dynamically. Moreover, the degree of freedom and transmission system reliability should have been further improved. Aiming at the above problems, this paper extracts the features of the combined signal from different degrees of freedom, considering the influence and effect of the distribution characteristics of the signal on its separability, and signal spatial structure to enhance the ability to adapt to complex environmental interference. It explores multiple interactive cooperative transmission scheduling to optimize the wireless resource dispatching and new network coding jointly, and get a unified multi antenna relay transmission processing standards and to improve the efficiency of information transmission. It carries out theoretical studies of interference coordination algorithms which can acquire higher forwarding efficiency at relay in the view of energy efficiency and spectral efficiency.
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