Maximum a Posteriori Probability (MAP) Joint Carrier Frequency Offset (CFO) and Channel Estimation for MIMO Channels with Spatial and Temporal Correlations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916927930105856 |
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| author | Khalife, Ibrahim Abbasi, Ali Feng, Zhe Zhou, Mingda Huang, Xinming Liu, Youjian |
| author_facet | Khalife, Ibrahim Abbasi, Ali Feng, Zhe Zhou, Mingda Huang, Xinming Liu, Youjian |
| contents | We consider time varying MIMO fading channels with known spatial and temporal correlation and solve the problem of joint carrier frequency offset (CFO) and channel estimation with prior distributions. The maximum a posteriori probability (MAP) joint estimation is proved to be equivalent to a separate MAP estimation of the CFO followed by minimum mean square error (MMSE) estimation of the channel while treating the estimated CFO as true. The MAP solution is useful to take advantage of the estimates from the previous data packet. A low complexity universal CFO estimation algorithm is extended from the time invariant case to the time varying case. Unlike past algorithms, the universal algorithm does not need phase unwrapping to take advantage of the full range of symbol correlation and achieves the derived Bayesian Cramér-Rao lower bound (BCRLB) in almost all SNR range. We provide insight on the the relation among the temporal correlation coefficient of the fading, the CFO estimation performance, and the pilot signal structure. An unexpected observation is that the BCRLB is not a monotone function of the temporal correlation and is strongly influenced by the pilot signal structures. A simple rearrangement of the 0's and 1's in the pilot signal matrix will render the BCRLB from being non-monotone to being monotone in certain temporal correlation ranges. Since the BCRLB is shown to be achieved by the proposed algorithm, it provides a guideline for pilot signal design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Maximum a Posteriori Probability (MAP) Joint Carrier Frequency Offset (CFO) and Channel Estimation for MIMO Channels with Spatial and Temporal Correlations Khalife, Ibrahim Abbasi, Ali Feng, Zhe Zhou, Mingda Huang, Xinming Liu, Youjian Information Theory We consider time varying MIMO fading channels with known spatial and temporal correlation and solve the problem of joint carrier frequency offset (CFO) and channel estimation with prior distributions. The maximum a posteriori probability (MAP) joint estimation is proved to be equivalent to a separate MAP estimation of the CFO followed by minimum mean square error (MMSE) estimation of the channel while treating the estimated CFO as true. The MAP solution is useful to take advantage of the estimates from the previous data packet. A low complexity universal CFO estimation algorithm is extended from the time invariant case to the time varying case. Unlike past algorithms, the universal algorithm does not need phase unwrapping to take advantage of the full range of symbol correlation and achieves the derived Bayesian Cramér-Rao lower bound (BCRLB) in almost all SNR range. We provide insight on the the relation among the temporal correlation coefficient of the fading, the CFO estimation performance, and the pilot signal structure. An unexpected observation is that the BCRLB is not a monotone function of the temporal correlation and is strongly influenced by the pilot signal structures. A simple rearrangement of the 0's and 1's in the pilot signal matrix will render the BCRLB from being non-monotone to being monotone in certain temporal correlation ranges. Since the BCRLB is shown to be achieved by the proposed algorithm, it provides a guideline for pilot signal design. |
| title | Maximum a Posteriori Probability (MAP) Joint Carrier Frequency Offset (CFO) and Channel Estimation for MIMO Channels with Spatial and Temporal Correlations |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.01032 |