A semi-blind joint channel estimation and equalization single carrier coherent underwater acoustic communication receiver

Based on Gibbs sampling technology, this paper puts forward a new semi-blind joint channel estimation and equalization algorithm that used in signal carrier coherent underwater acoustic communication system. First of all, we estimate the channel by using the train sequence with a loop structure before the data, and according to the sparse characteristics of underwater acoustic channel, this paper uses the sparse learning via iterative minimization (SLIM) algorithm which improves the accuracy of channel estimation under low SNR, and results in an accurate estimation with accurate SNR. Second, in dealing with the data frame, based on the time-varying characteristics of the underwater acoustic channel, the data frame is divided into specific length of overlapping pieces for processing. In overlapping blocks, based on the channel estimation results, we will get the maximum a posteriori (MAP) probability of the send data, at the same time; we will use the new information to update the channel at the same time, tracking the change of the channel. We analyzed the data getting in the ocean experiment of Xiamen sea area of 10 km, the results show that compared with traditional frequency domain linear equalization and time domain of decision feedback equalizer (DFE) algorithm, the algorithm is robust and the bit error rate is significantly reduced.