High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems
Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large nu...
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| Veröffentlicht in: | Electronics (Basel) Jg. 11; H. 22; S. 3806 |
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| Abstract | Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity. |
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| AbstractList | Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity. |
| Audience | Academic |
| Author | Khoso, Imran A. Ahmad, Mushtaq Zhang, Xiaofei Shi, Xinlei Qian, Yang |
| Author_xml | – sequence: 1 givenname: Mushtaq orcidid: 0000-0003-4280-5766 surname: Ahmad fullname: Ahmad, Mushtaq – sequence: 2 givenname: Xiaofei surname: Zhang fullname: Zhang, Xiaofei – sequence: 3 givenname: Imran A. surname: Khoso fullname: Khoso, Imran A. – sequence: 4 givenname: Xinlei orcidid: 0000-0002-1004-386X surname: Shi fullname: Shi, Xinlei – sequence: 5 givenname: Yang surname: Qian fullname: Qian, Yang |
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| Cites_doi | 10.3390/electronics10050578 10.1109/MCOM.2014.6736761 10.1109/JSTSP.2014.2317671 10.3390/electronics8090980 10.1109/PIMRC.2017.8292627 10.1109/SiPS.2017.8109972 10.1109/TCSI.2018.2875741 10.1109/JSTSP.2014.2324534 10.1109/TWC.2010.092810.091092 10.1109/JSTSP.2014.2313021 10.1109/CC.2017.8233666 10.1109/JSTSP.2014.2314213 10.1109/JSAC.2013.130205 10.1109/ACCESS.2018.2889728 10.1007/s11265-018-1341-3 10.1016/j.dsp.2017.06.018 10.1109/JSEE.2013.00011 10.1002/ett.4566 10.1049/el.2020.0208 10.1016/j.cam.2003.11.012 10.1109/TVT.2022.3172950 10.1109/JSAC.2003.809458 10.1109/ISCE.2019.8901015 10.1109/CAMAD.2018.8514963 10.1109/MSP.2011.2178495 10.1109/LCOMM.2010.102610.101581 10.1049/cmu2.12331 10.1007/s11235-015-0112-8 10.1093/imamat/4.3.295 10.1070/SM1967v001n04ABEH001994 10.1109/6GSUMMIT49458.2020.9083809 10.1109/SiPS.2017.8109988 10.1007/978-3-319-28483-5 10.1007/978-3-030-42531-9_5 10.1109/TVT.2014.2370106 10.3390/electronics11010092 10.1109/TCOMM.2013.020413.110848 10.1109/TIT.2003.817444 10.1109/LCOMM.2015.2514281 |
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| SubjectTerms | Algorithms Antennas (Electronics) Complexity Efficiency Error detection Iterative methods Methods MIMO communication MIMO communications Receivers & amplifiers Sensors Signal detection Signal processing |
| Title | High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems |
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