Blind Joint Maximum Likelihood Channel Estimation and Data Detection for Single-Input Multiple-Output Systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization over channel and data is decomposed into an iterative optimization loop. An efficient global optimization algorithm called the repeated weighted boosting search is employed at the upper level to identify optimally the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimization scheme for blind adaptive SIMO systems. Our simulation study shows that this scheme requires very few received data samples to achieve a near optimal solution of the joint ML SIMO channel estimation and data detection.