A stochastic analysis of an iterative semi-blind beamformer for TDMA systems

We describe an iterative semi-blind (SB) beamforming algorithm for time-division multiple-access (TDMA) systems and analyze its performance using a stochastic model. A least-squares approximation for constant modulus signals leads to a relatively simple and practical SB algorithm. The beamformer weights are calculated using a two-step procedure: initial weights are computed using training data and these are refined by the SB algorithm. The main goal of this paper is to analyze the beamforming algorithm using Wiener filter theory. In one approach, we develop a stochastic model using a Gaussian approximation that simplifies a fourth-order moment of the array data. We also provide an exact analysis and compare the results with that of the Gaussian approximation. Furthermore, we compare the proposed SB algorithm with the regularized semi-blind (RSB) technique using simulated and real TDMA data. The performance of the SB algorithm is also analyzed with short as well as long training data sequences in asynchronous interference scenarios. Experimental results show that the proposed SB beamforming algorithm suppresses cochannel interference affecting the coded data, and outperforms the RSB algorithm.