An interleaved iterative-structured joint detection algorithm for active users and frequency offsets in grant-free massive access
Great-free user access is an efficient access method for massive machine-type communications (mMTC). In the massive grant-free access, frequency offsets between users and base stations lead to the degradation of active user detection and channel estimation performance. Although traditional methods a...
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| Vydáno v: | Physical communication Ročník 71; s. 102697 |
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| Jazyk: | angličtina |
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Elsevier B.V
01.08.2025
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| ISSN: | 1874-4907 |
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| Abstract | Great-free user access is an efficient access method for massive machine-type communications (mMTC). In the massive grant-free access, frequency offsets between users and base stations lead to the degradation of active user detection and channel estimation performance. Although traditional methods achieve joint estimation by extending the perceptual matrix using the grid method, they all ignore the effect of channel fading on joint detection, which can seriously degrade the accuracy of detection. In this paper, we propose an interleaved iterative-structured-vector approximation message passing (VAMP) algorithm, which makes use of the structuring of the extended perceptual matrix, and designs a minimum mean square error (MMSE) nonlinear vector noise reducer based on the Bayesian principle to eliminate the effect of channel fading on detection. In addition, in order to improve the detection accuracy, a two-layer alternating iterative search method is proposed, which effectively overcomes the performance loss caused by the frequency offset of the grid method estimation. Simulation results show that the proposed scheme is superior in active user detection and frequency offset detection accuracy compared with the traditional schemes. |
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| AbstractList | Great-free user access is an efficient access method for massive machine-type communications (mMTC). In the massive grant-free access, frequency offsets between users and base stations lead to the degradation of active user detection and channel estimation performance. Although traditional methods achieve joint estimation by extending the perceptual matrix using the grid method, they all ignore the effect of channel fading on joint detection, which can seriously degrade the accuracy of detection. In this paper, we propose an interleaved iterative-structured-vector approximation message passing (VAMP) algorithm, which makes use of the structuring of the extended perceptual matrix, and designs a minimum mean square error (MMSE) nonlinear vector noise reducer based on the Bayesian principle to eliminate the effect of channel fading on detection. In addition, in order to improve the detection accuracy, a two-layer alternating iterative search method is proposed, which effectively overcomes the performance loss caused by the frequency offset of the grid method estimation. Simulation results show that the proposed scheme is superior in active user detection and frequency offset detection accuracy compared with the traditional schemes. |
| ArticleNumber | 102697 |
| Author | Cui, Zhihao Song, Yujie Li, Shibao Tang, Ziyi Liu, Jianhang Cui, Xuerong |
| Author_xml | – sequence: 1 givenname: Shibao orcidid: 0000-0002-3924-9001 surname: Li fullname: Li, Shibao email: lishibao@upc.edu.cn – sequence: 2 givenname: Zhihao surname: Cui fullname: Cui, Zhihao email: Z22160071@s.upc.edu.cn – sequence: 3 givenname: Yujie surname: Song fullname: Song, Yujie email: Z22160074@s.upc.edu.cn – sequence: 4 givenname: Ziyi surname: Tang fullname: Tang, Ziyi email: Z23160096@s.upc.edu.cn – sequence: 5 givenname: Xuerong surname: Cui fullname: Cui, Xuerong email: cxr@upc.edu.cn – sequence: 6 givenname: Jianhang surname: Liu fullname: Liu, Jianhang email: liujianhang@upc.edu.cn |
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| Cites_doi | 10.1109/MCOM.2014.6736746 10.1109/TSP.2020.2967175 10.1109/ACCESS.2018.2837382 10.1016/j.sigpro.2016.08.027 10.1109/TCOMM.2022.3151775 10.1016/j.phycom.2016.07.001 10.1109/JSAC.2020.3018802 10.1109/TSP.2018.2795540 10.1109/ACCESS.2019.2931563 10.1109/TSP.2018.2818082 10.1073/pnas.0909892106 10.1109/TCOMM.2018.2866559 10.1109/LWC.2019.2947036 10.1109/TWC.2021.3121066 10.1016/j.phycom.2019.100987 10.1109/MSP.2018.2844952 10.1109/MCOM.2013.6525600 10.1109/TWC.2021.3125199 10.1109/MWC.2016.1500284WC 10.1109/TVT.2018.2859806 10.1109/TWC.2017.2747145 10.1109/LCOMM.2016.2624297 |
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| Keywords | Approximate message passing Active user detection Frequency offset estimation mMTC Channel estimation Grant-free |
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| SubjectTerms | Active user detection Approximate message passing Channel estimation Frequency offset estimation Grant-free mMTC |
| Title | An interleaved iterative-structured joint detection algorithm for active users and frequency offsets in grant-free massive access |
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