Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916865014497280 |
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| author | Wang, Dezhi Huang, Chongwen Yuan, Xiaojun Muhaidat, Sami Liu, Lei Chen, Xiaoming Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| author_facet | Wang, Dezhi Huang, Chongwen Yuan, Xiaojun Muhaidat, Sami Liu, Lei Chen, Xiaoming Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| contents | Orthogonal delay-Doppler division multiplexing~(ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output~(MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing~(MAMP) to estimate the channel state information~(CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19812 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing Wang, Dezhi Huang, Chongwen Yuan, Xiaojun Muhaidat, Sami Liu, Lei Chen, Xiaoming Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane Signal Processing Orthogonal delay-Doppler division multiplexing~(ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output~(MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing~(MAMP) to estimate the channel state information~(CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error. |
| title | Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.19812 |