Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach
Chin-Hung Chen, Ivana Nikoloska, Wim J. van Houtum, Yan Jun Wu, Boris Karanov, Alex Alvarado · 2025
Impulsive noise (IN) commonly generated by power devices can severely degrade the performance of highsensitivity wireless receivers. Accurate channel state information (CSI) knowledge is essential for designing optimal maximum a posteriori detectors. This paper examines blind channel estimation methods based on the expectation-maximization (EM) algorithm tailored for scenarios impacted by bursty IN, which can be described by the Markov-Middleton model. We propose a constrained EM algorithm that exploits the trellis structure of the IN model and the transmitted binary phase-shift keying (BPSK) symbols. By enforcing shared variance among specific trellis states and symmetry in the transition matrix, the proposed constrained EM algorithm adapted for the bursty IN channel has an almost two times faster convergence rate and better estimation performance than the standard EM approach. We comprehensively evaluate the robustness of both standard and constrained EM estimators under different types of CSI uncertainties. The results indicate that the final estimations of both EM estimators are robust enough to mismatch MarkovMiddleton model parameters. However, as the level of CSI uncertainty increases, the convergence rate decreases.