Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems
In multiuser massive Multiple Input Multiple Output (MIMO) systems, a large amount of antennas are deployed at the Base Station (BS). In this case, the Minimum Mean Square Error (MMSE) detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion op...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822000529 |
| _version_ | 1797829546320330752 |
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| author | Souleymane Berthe Xiaorong Jing Hongqing Liu Qianbin Chen |
| author_facet | Souleymane Berthe Xiaorong Jing Hongqing Liu Qianbin Chen |
| author_sort | Souleymane Berthe |
| collection | DOAJ |
| description | In multiuser massive Multiple Input Multiple Output (MIMO) systems, a large amount of antennas are deployed at the Base Station (BS). In this case, the Minimum Mean Square Error (MMSE) detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation. The optimization algorithms such as Gradient Descent (GD) method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation. However, they converge slowly when the condition number of the MMSE filtering matrix (the coefficient matrix) increases, which can compromise the efficiency of their implementation. Moreover, their soft information computation also involves a large-scale matrix-matrix multiplication operation. In this paper, a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent (APGD-SOD) method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems. In the proposed detector, an Adaptive Pre-conditioner (AP) matrix obtained through the Quasi-Newton Symmetric Rank One (QN-SR1) update in each iteration is used to accelerate the convergence of the GD method. The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix, since then the condition number would be close to unity and the convergence would be rapid. By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems, the computational complexity of the soft information is simplified. The proposed AP matrix is applied to the GD method as a showcase. However, it also can be used by Conjugate Gradient (CG) method due to its generality. It is demonstrated that the proposed detector is robust and its convergence rate is superlinear. Simulation results show that the proposed detector converges at most four iterations. Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation, which entails a big challenge for the efficient implementation. |
| first_indexed | 2024-04-09T13:21:56Z |
| format | Article |
| id | doaj.art-56233a032ec94a698a1c7224ff2351ea |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-09T13:21:56Z |
| publishDate | 2023-04-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Digital Communications and Networks |
| spelling | doaj.art-56233a032ec94a698a1c7224ff2351ea2023-05-11T04:24:16ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-04-0192557566Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systemsSouleymane Berthe0Xiaorong Jing1Hongqing Liu2Qianbin Chen3School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing, 400065, China; Corresponding author.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing, 400065, ChinaChongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing, 400065, ChinaIn multiuser massive Multiple Input Multiple Output (MIMO) systems, a large amount of antennas are deployed at the Base Station (BS). In this case, the Minimum Mean Square Error (MMSE) detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation. The optimization algorithms such as Gradient Descent (GD) method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation. However, they converge slowly when the condition number of the MMSE filtering matrix (the coefficient matrix) increases, which can compromise the efficiency of their implementation. Moreover, their soft information computation also involves a large-scale matrix-matrix multiplication operation. In this paper, a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent (APGD-SOD) method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems. In the proposed detector, an Adaptive Pre-conditioner (AP) matrix obtained through the Quasi-Newton Symmetric Rank One (QN-SR1) update in each iteration is used to accelerate the convergence of the GD method. The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix, since then the condition number would be close to unity and the convergence would be rapid. By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems, the computational complexity of the soft information is simplified. The proposed AP matrix is applied to the GD method as a showcase. However, it also can be used by Conjugate Gradient (CG) method due to its generality. It is demonstrated that the proposed detector is robust and its convergence rate is superlinear. Simulation results show that the proposed detector converges at most four iterations. Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation, which entails a big challenge for the efficient implementation.http://www.sciencedirect.com/science/article/pii/S2352864822000529Multiuser massive MIMOMMSE algorithmGD MethodSoft-outputPre-conditioningSymmetric rank one update |
| spellingShingle | Souleymane Berthe Xiaorong Jing Hongqing Liu Qianbin Chen Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems Digital Communications and Networks Multiuser massive MIMO MMSE algorithm GD Method Soft-output Pre-conditioning Symmetric rank one update |
| title | Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems |
| title_full | Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems |
| title_fullStr | Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems |
| title_full_unstemmed | Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems |
| title_short | Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems |
| title_sort | low complexity soft output signal detector based on adaptive pre conditioned gradient descent method for uplink multiuser massive mimo systems |
| topic | Multiuser massive MIMO MMSE algorithm GD Method Soft-output Pre-conditioning Symmetric rank one update |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822000529 |
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