Abstract
The scaling up of antenna and terminals in large-scale multiple-input multiple-output (massive MIMO) systems helps increasing the spectral efficiency at the penalty of prohibitive computational complexity. Linear precoders, such as the minimum mean square error (MMSE) precoding, can achieve the near-optimal performance in massive MIMO systems due to the asymptotic orthogonality channel matrix property, which makes them more attractive. But these precoders suffer from higher computational complexity due to the required matrix inversion. So, we propose a symmetric successive over-relaxation (SSOR) method-based MMSE precoding referred to as MSSR algorithm to avoid the complicated matrix inversion in an iterative way, and it can approach the performance of the classical MMSE. Simulation results show that the proposed algorithm can also approach the classical MMSE precoding performance with small number of iterations.
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Acknowledgements
This work is supported by Science and Technology Foundation of Jilin Province (No. 20180101039JC), and Science and Technology Foundation of Jilin City (No. 201831775).
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Li, J., Saeed, S.I.A., Yang, T., Xie, Y., Zhang, G. (2021). Low-Complexity MMSE Precoding Based on SSOR Iteration for Large-Scale Massive MIMO Systems. In: Pan, JS., Li, J., Namsrai, OE., Meng, Z., Savić, M. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 211. Springer, Singapore. https://doi.org/10.1007/978-981-33-6420-2_47
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DOI: https://doi.org/10.1007/978-981-33-6420-2_47
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