Research On Signal Detection Algorithms Based On The Antennas Arraying Architecture

Cognitive Radio(CR)is a promising technique which focused on solving the problem of spectrum resource scarcity caused by fixed spectrum allocation.Signal detection is one of the key technologies in Cognitive Radio,which had been studied by various academic communities in recent years.Signals transmitted through wireless channels will be affected by a variety of factors,such as multipath effect and shadow fading,etc.However,it is hard to fulfill system requirements with only one antenna at the receive end.To improve the accuracy and robustness of signal detection,it is reasonable to adopt a receive structure which consists multiple antennas.In this paper,we focused on signal detection technology based on antennas arraying,our work are mainly organized as follows:1.On condition that the noise power is known,the classical multi-antenna energy detection algorithm does not make full use of the spatial correlation information of the multi-antenna signals in the signal detection.To solve this problem,a joint detection algorithm based on cross correlation is proposed.The algorithm is based on the correlation values between the received data of each antenna,and designs the decision statistics.The algorithm is used to detect the existence of the original signal by comparing the threshold with the false alarm probability.The simulation results show that the proposed algorithm has a higher detection probability than the traditional multi-antenna energy detection scheme,with low signal to noise ratio,small observation data and low false alarm probability.2.In the multi-antenna uniform array,on condition that the noise variances are unknown,the noise power estimation error makes the detection performance of the multi-antenna energy detection method deteriorate sharply.The eigenvalue detection algorithm utilizes the spatial correlation of the multi-antenna signal,but failed to fully consider the difference of the multi-antenna signal about the delay,phase and so on.In this paper,a new multi-antenna signal blind detection algorithm based on equal gain combining is proposed.The algorithm considers the differences of the signals received by each antenna in the actual environment,applies the multi-antenna combining technology to the signal detection,and constructs a detection statistic independent of the noise power.Theoretical analysis and simulation results show that the proposed algorithm is superior to the multi-antenna energy detection method and eigenvalue detection method when the noise power is unknown.3.A multi-antenna signal detection algorithm with fixed threshold is given under the condition of multi-antenna non-uniform array and unknown noise power.The theoretical basis of the algorithm is that the eigenvalues of the covariance matrices of the antenna data have different characteristics in the iterative process of delay and phase alignment when the multi-antenna signal exists or not.The simulation results show that the proposed algorithm can effectively solve the problem of signal detection for the multi-antenna signals with different signal-to-noise ratio under when the noise power is unknown,and improve the detection performance compared with multi-antenna energy detection method.4.A multi-antenna signal detection algorithm is proposed based on the combination of the optimal weight in the frequency domain and the eigenvalue detection.The algorithm first combines each antenna signal in the frequency domain with the optimal weight.Then,based on the eigenvalues of the autocorrelation matrix of the combined signal,the new detection statistic is constructed,and the detection threshold is deduced by the stochastic matrix theory,and the final judgment is dependent on the comparison of the detection threshold and the detection statistic.The theoretical analysis and simulation results show that the algorithm makes full use of the correlation of the signal,and improves the signal quality by using the optimal weight synthesis in the frequency domain,thus improving the signal detection performance.