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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21340 |
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| _version_ | 1866917106501550080 |
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| author | Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Alvarado, Alex |
| author_facet | Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Alvarado, Alex |
| contents | This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM Chen, Chin-Hung Nikoloska, Ivana van Houtum, Wim Wu, Yan Alvarado, Alex Signal Processing Machine Learning This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations. |
| title | Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2511.21340 |