Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914900416135168 |
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| author | Schmid, Luca Raviv, Tomer Shlezinger, Nir Schmalen, Laurent |
| author_facet | Schmid, Luca Raviv, Tomer Shlezinger, Nir Schmalen, Laurent |
| contents | We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes significantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_02312 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation Schmid, Luca Raviv, Tomer Shlezinger, Nir Schmalen, Laurent Information Theory Machine Learning Signal Processing We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes significantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios. |
| title | Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2408.02312 |