Semi-blind Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems

Semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multiple-input multiple-output (MIMO) systems. The joint ML optimization over channel and data is decomposed into an iterative two-level optimization loop. An efficient optimization search algorithm referred to as the repeated weighted boosting search (RWBS) is employed at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector termed as the optimized hierarchy reduced search algorithm is used at the lower level to perform ML detection of the transmitted data. Only a minimum pilot overhead is required to aid the RWBS channel estimator´s initial operation, which not only speeds up convergence but also avoids ambiguities inherent in blind joint estimation of both the channel and data.