Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems
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| Format: | Preprint |
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2025
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| _version_ | 1866911087824207872 |
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| author | Zhao, Zilu Forsch, Christian Cottatellucci, Laura Slock, Dirk |
| author_facet | Zhao, Zilu Forsch, Christian Cottatellucci, Laura Slock, Dirk |
| contents | Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) is considered one of the leading candidates for enabling next-generation wireless communication. With the growing interest in the Internet of Things (IoT), the Grant-Free (GF) access scheme has emerged as a promising solution to support massive device connectivity. The integration of GF and CF-MaMIMO introduces significant challenges, particularly in designing distributed algorithms for activity detection and pilot contamination mitigation. In this paper, we propose a distributed algorithm that addresses these challenges. Our method first employs a component-wise iterative distributed Maximum Likelihood (ML) approach for activity detection, which considers both the pilot and data portions of the received signal. This is followed by a Pseudo-Prior Hybrid Variational Bayes and Expectation Propagation (PP-VB-EP) algorithm for joint data detection and channel estimation. Compared to conventional VB-EP, the proposed PP-VB-EP demonstrates improved convergence behavior and reduced sensitivity to initialization, especially when data symbols are drawn from a finite alphabet. The pseudo prior used in PP-VB-EP acts as an approximated posterior and serves as a regularization term that prevents the Message Passing (MP) algorithm from diverging. To compute the pseudo prior in a distributed fashion, we further develop a distributed version of the Variable-Level Expectation Propagation (VL-EP) algorithm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_21363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems Zhao, Zilu Forsch, Christian Cottatellucci, Laura Slock, Dirk Information Theory Signal Processing Applications Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) is considered one of the leading candidates for enabling next-generation wireless communication. With the growing interest in the Internet of Things (IoT), the Grant-Free (GF) access scheme has emerged as a promising solution to support massive device connectivity. The integration of GF and CF-MaMIMO introduces significant challenges, particularly in designing distributed algorithms for activity detection and pilot contamination mitigation. In this paper, we propose a distributed algorithm that addresses these challenges. Our method first employs a component-wise iterative distributed Maximum Likelihood (ML) approach for activity detection, which considers both the pilot and data portions of the received signal. This is followed by a Pseudo-Prior Hybrid Variational Bayes and Expectation Propagation (PP-VB-EP) algorithm for joint data detection and channel estimation. Compared to conventional VB-EP, the proposed PP-VB-EP demonstrates improved convergence behavior and reduced sensitivity to initialization, especially when data symbols are drawn from a finite alphabet. The pseudo prior used in PP-VB-EP acts as an approximated posterior and serves as a regularization term that prevents the Message Passing (MP) algorithm from diverging. To compute the pseudo prior in a distributed fashion, we further develop a distributed version of the Variable-Level Expectation Propagation (VL-EP) algorithm. |
| title | Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems |
| topic | Information Theory Signal Processing Applications |
| url | https://arxiv.org/abs/2507.21363 |