Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916983952375808 |
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| author | Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Karanov, Boris Alvarado, Alex |
| author_facet | Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Karanov, Boris Alvarado, Alex |
| contents | Impulsive noise (IN) commonly generated by power devices can severely degrade the performance of high sensitivity 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 Markov-Middleton model parameters. However, as the level of CSI uncertainty increases, the convergence rate decreases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Karanov, Boris Alvarado, Alex Signal Processing Information Theory Machine Learning Impulsive noise (IN) commonly generated by power devices can severely degrade the performance of high sensitivity 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 Markov-Middleton model parameters. However, as the level of CSI uncertainty increases, the convergence rate decreases. |
| title | Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2504.03685 |