Belief Information based Deep Channel Estimation for Massive MIMO Systems
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911951071739904 |
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| author | Xu, Jialong Liu, Liu Wang, Xin Chen, Lan |
| author_facet | Xu, Jialong Liu, Liu Wang, Xin Chen, Lan |
| contents | In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. However, current MIMO systems rely on inserting pilot signals to achieve accurate channel estimation. As the increase of transmit stream, the pilots consume a significant portion of transmission resources, severely reducing the spectral efficiency. In this correspondence, we propose a belief information based mechanism. By introducing a plug-and-play belief information module, existing single-antenna channel estimation networks could be seamlessly adapted to multi-antenna channel estimation and fully exploit the spatial correlation among multiple antennas. Experimental results demonstrate that the proposed method can either improve 1 ~ 2 dB channel estimation performance or reduce 1/3 ~ 1/2 pilot overhead, particularly in bad channel conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07744 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Belief Information based Deep Channel Estimation for Massive MIMO Systems Xu, Jialong Liu, Liu Wang, Xin Chen, Lan Information Theory Artificial Intelligence Signal Processing In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. However, current MIMO systems rely on inserting pilot signals to achieve accurate channel estimation. As the increase of transmit stream, the pilots consume a significant portion of transmission resources, severely reducing the spectral efficiency. In this correspondence, we propose a belief information based mechanism. By introducing a plug-and-play belief information module, existing single-antenna channel estimation networks could be seamlessly adapted to multi-antenna channel estimation and fully exploit the spatial correlation among multiple antennas. Experimental results demonstrate that the proposed method can either improve 1 ~ 2 dB channel estimation performance or reduce 1/3 ~ 1/2 pilot overhead, particularly in bad channel conditions. |
| title | Belief Information based Deep Channel Estimation for Massive MIMO Systems |
| topic | Information Theory Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2407.07744 |