Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems
Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss–Seidel (GS...
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12638 |
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| author | Dong Shen Li Chen Hao Liang |
| author_facet | Dong Shen Li Chen Hao Liang |
| author_sort | Dong Shen |
| collection | DOAJ |
| description | Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss–Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme’s BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U<sup>2</sup>), whereas the MMSE detection algorithm has a computational complexity of O(U<sup>3</sup>), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity. |
| first_indexed | 2024-03-09T01:55:33Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:33Z |
| publishDate | 2023-11-01 |
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| spelling | doaj.art-3d3ed437b73e4873bf18e84eacbeaca22023-12-08T15:11:14ZengMDPI AGApplied Sciences2076-34172023-11-0113231263810.3390/app132312638Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO SystemsDong Shen0Li Chen1Hao Liang2School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730000, ChinaSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730000, ChinaSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730000, ChinaSignal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss–Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme’s BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U<sup>2</sup>), whereas the MMSE detection algorithm has a computational complexity of O(U<sup>3</sup>), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity.https://www.mdpi.com/2076-3417/13/23/12638massive MIMOconjugate gradientJacobiGauss–SeidelKronecker channel |
| spellingShingle | Dong Shen Li Chen Hao Liang Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems Applied Sciences massive MIMO conjugate gradient Jacobi Gauss–Seidel Kronecker channel |
| title | Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems |
| title_full | Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems |
| title_fullStr | Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems |
| title_full_unstemmed | Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems |
| title_short | Fast Converging Gauss–Seidel Iterative Algorithm for Massive MIMO Systems |
| title_sort | fast converging gauss seidel iterative algorithm for massive mimo systems |
| topic | massive MIMO conjugate gradient Jacobi Gauss–Seidel Kronecker channel |
| url | https://www.mdpi.com/2076-3417/13/23/12638 |
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