Physics-Aware Initialization Refinement in Code-Aided EM for Blind Channel Estimation

This paper addresses the well-known local maximum problem of the expectation-maximization (EM) algorithm in blind inter-symbol interference (ISI) channel estimation. This problem primarily results from phase and shift ambiguity due to poor initialization, which a blind EM estimation is inherently unable to distinguish. We propose an effective initialization refinement algorithm that utilizes the decoder output as a metric for model selection. Finite candidate models are generated based on the physical properties of the channel and modulation format, incorporating a joint detection of phase and shift ambiguities. Our results show that the proposed algorithm significantly reduces the number of local maximum cases to nearly onethird for a 3-tap ISI channel under highly uncertain initial conditions. The improvement becomes more pronounced as initial errors increase and the channel memory grows. When used in a turbo equalizer, the proposed algorithm is required only in the first turbo iteration, which limits any complexity increase with subsequent iterations.