ML-Based Iterative Approach for Blind Frequency Domain Equalization and Combination Over Sparse Channels

Abstract

This letter deals with blind spatial diversity equalization and combination of sparse channels. The equalization and combination problem of multiple signals is modeled as the maximum likelihood estimation of frequency domain symbol sequence from incomplete observations, and solved by means of the expectation-maximization (EM) algorithm. Closed-form expression of the equalization output is obtained, which shows that the complicated problem of signal equalization and combination in multipath channels is converted to the weighted summation of each discrete-frequency signals, eliminating the need of complicated maximum likelihood sequence estimation (MLSE) or de-convolution demanded by time domain equalization. Simulation results show that the proposed scheme enables evident performance improvement in terms of symbol error rate especially at low signal-to-noise ratio (SNR) values and short signal lengths compared with a typical scheme.