SNR Estimation In Underlay Spectrum Sharing Mode

With car networking and internet of things bringing a large number of terminals and data access,and people's increasing demands for a wide variety of application experiences,spectrum resources are becoming more and more precious.In order to solve the contradiction between people's increasing spectrum demand and limited spectrum resources,more and more scholars have devoted themselves to the research of spectrum sharing.Underlay spectrum sharing technology which is considered as an effective method to solve the problem of the shortage of spectrum resources receives extensive attention.When the interference caused by cognitive users and the noise to authorized users is below a certain threshold,the technology allows authorized users and cognitive users to use licensed frequency bands at the same time,which improves spectrum utilization.The interference temperature,as the key basis for measuring the degree of interference,is the most critical prior information for achieving spectrum sharing.And the signal-to-noise ratio is an important parameter for calculating the interference temperature.So this paper mainly studies the SNR estimation of time-frequency overlapped signals in Underlay cognitive networks.The main contents of this paper are as follows: 1.In this paper,a signal-to-noise ratio estimation method based on normalized high-order cumulants is proposed.The method calculates the values of normalized high-order cumulants of different orders through sample signals and constructs a system of equations.It is proved that the normalized high-order cumulant of the signal has nothing to do with the signal power,but only related to the signal modulation type,roll-off factor and so on.The specific values of the normalized high-order cumulant can be obtained by querying the table.And according to the characteristic that the high-order cumulant of Gaussian white noise is zero,the ratio of the component signal power to the received signal power can be solved.Finally,the signal-to-noise ratio of the component signal and the time-frequency overlap signal can be obtained.This paper also derives the Cramer-Rao bound of the signal-to-noise ratio of the component signals,and the evaluation performance of SNR estimation methods proposed in this paper can be evaluated by comparing the normalized Cramer-Raman bound(NCRB)with the normalized mean square error of the actual estimated signal-to-noise ratio of the component signals.In this paper,the SNR estimation methods of time-frequency overlapping signals under the Underlay spectrum sharing methods are simulated and analyzed under the condition of different number of sources,different modulation types,different overlap ratios and different power ratios.The simulation results show that this SNR method proposed in this paper can effectively estimate the signal-to-noise ratio of the time-frequency overlapping signal in a low SNR condition,and it can also accurately estimate the signal-to-noise ratio of each component signal.In addition,this method is also robust to the modulation type combination and spectral overlap ratio of the component signals.2.In order to reduce the computational complexity,this paper also proposes a signal-to-noise ratio estimation algorithm for time-frequency overlapped signals based on second-order time-varying moments.This method proves that the second-order time-varying correlation of the shaping filter function is only related to roll-off factor,and the ratio of time delay and symbol period,and it is verified that the autocorrelation function is a continuous curve at the non-zero point.The signal samples are used to calculate the second-order time-varying moments of different time delays to obtain the power estimates of the component signals,and the signal-to-noise ratios of the component signals and the time-frequency overlapped signals are obtained.The experimental simulation results show that the method has good robustness to the number of sources and power ratio,and it can also obtain accurate estimation results under mixed Gaussian noise environment.Through comparison with other estimation methods,it can be seen that the SNR estimation method based on the second-order time-varying moments has better performance at low SNR.In addition,this methods is easy to calculate,because it avoids the complicated solution process of multivariate higher-order equations.