Research On SNR Estimation And FPGA Implementation In Meteor Burst Communication

Meteor Burst Communication (MBC) has many features, such as good ability of anti-jamming, good secrecy and far transmission distance. Because of fast-fading characteristic in the meteor channel, the data throughput is low. In order to improve data throughput, transmission rate should change adaptively according to meteor channel condition. Signal-to-noise ratio (SNR) estimation is the key technology for the adaptive variable-rate. In Meteor Burst Communication, the modulation and coding efficiency change adaptively with SNR. In this thesis, the SNR estimation and its FPGA realization for Meteor Burst Communication are comprehensively studied.Firstly, four typical SNR estimation algorithms are introduced, including the Second and Fourth Order Moments (M2M4) algorithm. Signal to Variation Ratio (SVR) algorithm, Maximum Likelihood (ML) algorithm and Squared Signal to Noise Variance (SNV) algorithm. Then the performances of the SNR estimation algorithms are quantitatively compared by the analysis of SNR estimates produced by computer simulation and the Cramer-Rao bound (CRB) of estimation is also discussed. Secondly, an improved SNV algorithm is proposed by considering the effect of random phase shift. Simulation results show that its performance is superior to the original SNV algorithm. Finally, the improved SNV algorithm is implemented on Altera’s Cyclone II EP2C50with VHDL language. The test results show that the method proposed in this thesis can reduce the effect of phase shift for better performance.