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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References
Rusek, F., et al.: Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Sig. Process. Mag. 30(1), 40–60 (2013)
Zheng, L., Tse, D.N.C.: Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Trans. Inf. Theory 49(5), 1073–1096 (2003)
Dai, X., et al.: Successive interference cancelation amenable multiple access (SAMA) for future wireless communications. In: 2014 IEEE International Conference on Communication Systems, Macau, pp. 222–226 (2014)
Marzetta, T.L.: Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wirel. Commun. 9(11), 3590–3600 (2010)
Lu, L., Li, G.Y., Swindlehurst, A.L., Ashikhmin, A., Zhang, R.: An overview of massive MIMO: benefits and challenges. IEEE J. Sel. Top. Sig. Process. 8(5), 742–758 (2014)
Xie, H., Gao, F., Zhang, S., Jin, S.: A unified transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model. IEEE Trans. Veh. Technol. 66(4), 3170–3184 (2017)
Boccardi, F., Heath, R.W., Lozano, A., Marzetta, T.L., Popovski, P.: Five disruptive technology directions for 5G. IEEE Commun. Mag. 52(2), 74–80 (2014)
Artes, H., Seethaler, D., Hlawatsch, F.: Efficient detection algorithms for MIMO channels: a geometrical approach to approximate ML detection. IEEE Trans. Signal Process. 51(11), 2808–2820 (2003)
Jalden, J., Ottersten, B.: On the complexity of sphere decoding in digital communications. IEEE Trans. Sig. Process. 53(4), 1474–1484 (2005)
Bello, I.A., Halak, B., El-Hajjar, M., Zwolinski, M.: VLSI implementation of a scalable K-best MIMO detector. In: 2015 15th International Symposium on Communications and Information Technologies (ISCIT), Nara, pp. 281–286 (2015)
Wu, M., Yin, B., Wang, G., Dick, C., Cavallaro, J.R., Studer, C.: Large-scale MIMO detection for 3GPP LTE: algorithms and FPGA implementations. IEEE J. Sel. Top. Sig. Process. 8(5), 916–929 (2014)
Zhu, D., Li, B., Liang, P.: On the matrix inversion approximation based on neumann series in massive MIMO systems. In: 2015 IEEE International Conference on Communications (ICC), London, pp. 1763–1769 (2015)
Hadjidimos, A.: Accelerated overrelaxation method. Math. Comput. 32(141), 149–157 (1978)
Bölcskei, H.: Space-Time Wireless Systems: From Array Processing to MIMO Communications. Cambridge University Press, New York (2006)
Xie, H., Gao, F., Jin, S.: An overview of low-rank channel estimation for massive MIMO systems. IEEE Access 4, 7313–7321 (2016)
Björck, Å.: Numerical Methods in Matrix Computations. TAM, vol. 59. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-05089-8
Zeng, W.: On convergence of AOR method. Journal of Huqiao University (1985)
Chen, P.X.: Convergence of AOR Method. Mathematica Numerica Sinica (1983)
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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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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