Abstract
Massive multiple-input multiple-output (MIMO) systems can substantially improve the spectral efficiency and system capacity by equipping a large number of antennas at the base station and it is envisaged to be one of the critical technologies in the next generation of wireless communication systems. However, the computational complexity of the signal detection in massive MIMO systems presents a significant challenge for practical hardware implementations. This work proposed a novel minimum mean square error (MMSE) signal detection method based on the accelerated overrelaxation (AOR) iterative algorithm. The proposed AOR-based method can reduce the overall complexity of the classical MMSE signal detection by an order of magnitude from \( {\rm O}\left( {K^{3} } \right) \) to \( {\rm O}\left( {K^{2} } \right) \), where \( K \) is the number of users. Numerical results illustrate that the proposed AOR-based algorithm can outperform the performance of the recently proposed Neumann series approximation-based algorithm and approach the conventional MMSE signal detection involving exact matrix inversion with significantly reduced complexity.
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Acknowledgments
This work was supported by Huawei Innovation Research Program, the key project of the National Natural Science Foundation of China (No. 61431001), the 5G research program of China Mobile Research Institute (Grant No. [2015] 0615), the open research fund of National Mobile Communications Research Laboratory Southeast University (No. 2017D02), Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology), the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, and Keysight.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, Z. et al. (2018). Low-Complexity MMSE Signal Detection Based on the AOR Iterative Algorithm for Uplink Massive MIMO Systems. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_36
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DOI: https://doi.org/10.1007/978-3-319-72823-0_36
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