Study On Spectrum Sensing Algorithms In Cognitive Radio Systems

Wireless communication technology has got rapid development in recent years. A large number of emerging wireless applications and dramatically increasing number of wireless communication devices lead to a tremendous demand of spectrum resources. However, the majority of the spectrum suitable for wireless transmission has been assigned completely. The remaining spectrum for new systems and new businesses is very little or no spectrum can be assigned, the shortage of spectrum resource is growing shortage daily. In order to improve the spectrum utilization and to solve the spectrum scarcity problem essentially, Joseph Mitola proposes the concept of cognitive radio. The core idea of cognitive radio is dynamic spectrum access. It allows the secondary user to opportunistically utilize the vacant spectrum when it does not cause intolerable interference the primary user to improve the spectrum utilization substantially. Spectrum sensing is one of the key technologies in cognitive radio which is deemed to the basis and premise to realize cognitive radio. This paper focuses on spectrum sensing algorithms in cognitive radio systems. The main work of this paper is as follows:(1)The research background and significance of spectrum sensing are elaborated. Research status at home and abroad is summarized. The fundamental knowledge of spectrum sensing is introduced.(2)Aiming at the problem that the current eigenvalue based spectrum sensing algorithms can not be applied into the single antenna system. This paper proposes a maximum-minimum eigenvalue detection algorithm using a single antenna (referred to as SMME algorithm). The temporal smoothing technique is utilized to form a data structure received by virtual multi-antennas to get the sample covariance matrix. Then the maximum and minimum eigenvalues of the sample covariance matrix are calculated by exploiting the power method. The ratio of the maximum eigenvalue to the minimum eigenvalue is used as the test statistic. The decision threshold is derived based on random matrix theory. Finally, the test statistic is compared to the decision threshold to determine whether the primary signal is present or not. The proposed algorithm not only has the high reliable detection performance as the maximum-minimum eigenvalue detection algorithm using multiple antennas, but also has other advantages such as low computational complexity and easy implementation.(3)Aiming at the problem that the performance of energy detection algorithm is susceptible to noise uncertainty. This paper proposes a spectrum sensing algorithm based on correlation coefficients (referred to as CCSS algorithm). Firstly, the proposed algorithm utilizes a single antenna with a delay device to acquire the original received signal and the delayed received signal. Then the correlation coefficient of the two signals is calculated and the result is used as the test statistic. Finally, the test statistic is compared to the preset decision threshold to determine whether the primary signal is present or not. Theoretical analysis shows that the decision threshold of CCSS algorithm is not related to noise power, thus it can effectively overcome the influence of noise uncertainty on the detection performance. Simulation results show that CCSS algorithm has an excellent detection performance even under low signal-to-noise conditions.(4)The implementation process of the proposed spectrum sensing algorithms on Lyrtech platform is studied. The emitting and receiving process of the signal is realized in D/A board and A/D board, respectively. On this basis, the hardware simulations of SMME algorithm and CCSS algorithm are conducted to verify the effectiveness of the proposed algorithms.