The Research On Carrier Synchronization Of Short Burst Turbo Coded System

Short burst data transmission is a main transmission mode in modern communication system, which has been widely applied to military communications, satellite communications and civil mobile cellular communications. In the burst-mode transmission, it is difficult to obtain carrier synchronization in the receiver due to the worse wireless channel environment and variant channel parameters. At the same time, the appearance and application of effective channel code, such as turbo code as a representation, make the signal-to-noise ratio(SNR) requirement for data transmission to be very low. And the synchronization of turbo coded signals at low SNR conditions is a big challenge for burst-mode data transmission.In order to solve the problems presented above, firstly the principle of turbo coding and decoding is introduced, and the simulation analysis is done for the design parameters of Turbo code and the influence of frequency offset and phase offset to the system performance. Then carrier synchronization algorithms based on the soft information for short burst-mode transmission at low SNR are studied. The two algorithms are the joint date aided and maximum-mean-square soft-output algorithm and the maximum likelihood iterative soft-decision-directed algorithm. First, the joint date aided and maximum-mean-square soft-output algorithm makes coarse carrier estimation based ML method using pilot symbols, and compensates the received signals with the coarse estimated phase offset, and then obtains the fine estimated carrier frequency and phase offset by maximizing the mean-square soft-output function in one-dimension-search. The maximum likelihood iterative soft-decision-directed algorithm firstly modifies the turbo decoder, and makes the soft-decision symbols approximate the modulated symbol by a hyperbolic tangent function, and finally carrier frequency and phase offsets are estimated with maximum likelihood method. Simulation results show that:these two algorithms can achieve near-ideal decoding performance and are easy to implement, the estimation precision is high, and the bit error rate performance is also well.