Model-Free Channel Estimation for Massive MIMO: A Channel Charting-Inspired Approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918180604084224 |
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| author | Zheng, Pinjun Hossain, Md. Jahangir Chaaban, Anas |
| author_facet | Zheng, Pinjun Hossain, Md. Jahangir Chaaban, Anas |
| contents | Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise channel models that may not always be practical. This paper proposes a model-free approach combining deep autoencoders and LSTM networks. The method first learns low-dimensional channel representations preserving temporal correlation through augmenting a channel charting-inspired loss function, then tracks these features to recover full channel information from limited pilots. Simulation results using ray-tracing datasets show that the proposed approach achieves up to 9 dB improvement in normalized mean square error compared to the least-squares methods under ill-conditioned scenarios, while maintaining scalability across MIMO configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Free Channel Estimation for Massive MIMO: A Channel Charting-Inspired Approach Zheng, Pinjun Hossain, Md. Jahangir Chaaban, Anas Signal Processing Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise channel models that may not always be practical. This paper proposes a model-free approach combining deep autoencoders and LSTM networks. The method first learns low-dimensional channel representations preserving temporal correlation through augmenting a channel charting-inspired loss function, then tracks these features to recover full channel information from limited pilots. Simulation results using ray-tracing datasets show that the proposed approach achieves up to 9 dB improvement in normalized mean square error compared to the least-squares methods under ill-conditioned scenarios, while maintaining scalability across MIMO configurations. |
| title | Model-Free Channel Estimation for Massive MIMO: A Channel Charting-Inspired Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.00225 |