Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916346547142656 |
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| author | Khan, Muhammad Usman Testi, Enrico Chiani, Marco Paolini, Enrico |
| author_facet | Khan, Muhammad Usman Testi, Enrico Chiani, Marco Paolini, Enrico |
| contents | Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is "blind", i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks Khan, Muhammad Usman Testi, Enrico Chiani, Marco Paolini, Enrico Signal Processing Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is "blind", i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method. |
| title | Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.02359 |