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ISSN Печать: 0040-2508
ISSN Онлайн: 1943-6009
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LOW-COMPLEXITY ITERATIVE DETECTION BASED ON PARAMETRIC AOR FOR UPLINK MASSIVE MIMO SYSTEMS
Краткое описание
Currently, massive multiple-input multiple-output (MIMO) is one of the most enthralling wireless access technologies to deliver the needs of 5G and beyond networks. Massive MIMO requires handling large scale matrix computation, especially for matrix inversion. Linear minimum mean-square error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. In this paper, we propose a novel LMMSE signal detection for an uplink massive MIMO system using the parametric accelerated over relaxation (PAOR) iterative method without complicated matrix inversion, which is capable of reducing the complexity of the classical LMMSE algorithm. Also, the initial solution is proposed by exploiting the benefits of a stair matrix to obtain a high performance, fast convergence rate, and low complexity. Simulation results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with low computational complexity. Also, we find that matrix inversion based on PAOR iteration is the practical solution for data detection in massive MIMO systems.
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Marzetta, T.L., (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas, IEEE transactions on wireless communications, 9(11), pp. 3590-3600.
-
Wu, H. et al. (2017) Low-complexity detection algorithms based on matrix partition for massive MIMO, 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2017, pp. 1-6.
-
Hoydis, J., Ten Brink, S., and Debbah, M., (2013) Massive MIMO in the UL/DL of cellular networks: How many antennas do we need? IEEE Journal on selected Areas in Communications, 31(2), pp. 160-171.
-
Busari, S.A. et al. (2019) Terahertz massive MIMO for beyond-5G wireless communication, in ICC IEEE International Conference on Communications (ICC), Shanghai, China, pp. 1-6.
-
Tan, X. et al., (2019) Enhanced linear iterative detector for massive multiuser MIMO uplink, IEEE Transactions on Circuits and Systems I: Regular Papers, 67(2), pp. 540-552.
-
Krishnamoorthy, A. and Menon, D., (2013) Matrix inversion using Cholesky decomposition, IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, pp. 70-72.
-
Ma, L. et al., (2011) QR decomposition-based matrix inversion for high performance embedded MIMO receivers, IEEE Transactions on Signal Processing, 59(4), pp. 1858-1867.
-
Yin, B. et al., (2013) Implementation trade-offs for linear detection in large-scale MIMO systems, IEEE International Conference on Acoustics, Speech and Signal Processing,Vancouver, BC, pp. 2679-2683.
-
Zhao, S., Shen, B., and Hua, Q., (2018) A comparative study of low-complexity MMSE signal detection for massive MIMO systems, TIIS, 12(4), pp. 1504-1526.
-
Wu, M. et al., (2014) Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations, IEEE Journal of Selected Topics in Signal Processing, 8(5), pp. 916-929.
-
Gao, X. et al., (2014) Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems, IEEE Global Communications Conference, Austin, TX, pp. 3291-3295.
-
Wang, F. et al., (2015) Efficient iterative soft detection based on polynomial approximation for massive MIMO, International Conference on Wireless Communications & Signal Processing (WCSP), Nanjing, pp. 1-5.
-
Minango, J. and de Almeida, C., (2017) Optimum and quasi-optimum relaxation parameters for low-complexity massive MIMO detector based on Richardson method, Electronics Letters, 53(16), pp. 1114-1115.
-
Kong, B.Y. and Park., I.-C., (2016) Low-complexity symbol detection for massive MIMO uplink based on Jacobi method, IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, pp. 1-5.
-
Zhang, Z. et al., (2017) A low-complexity signal detection utilizing AOR iterative method for massive MIMO systems, China Communications, 14(11), pp. 269-278.
-
Jiang, F. et al., (2018) Stair matrix and its applications to massive MIMO uplink data detection,IEEE Transactions on Communications, 66(6), pp. 2437-2455.
-
Vatti, V.K., Rao, G.C., and Pai, S.S., (2020) Parametric Accelerated Over Relaxation (PAOR) Method, in Numerical Optimization in Engineering and Sciences, Springer, pp. 283-288.
-
Ma, X. and Zhou, Q., (2014) Massive MIMO and its detection, in MIMO Processing for 4G and Beyond: Fundamentals and Evolution,CRC Press, pp. 449-472.
-
Ngo, H.Q., Larsson, E.G., and Marzetta, T.L., (2013) Energy and spectral efficiency of very large multiuser MIMO systems, IEEE Transactions on Communications, 61(4), pp. 1436-1449.
-
Luethi, P. et al., (2007) VLSI implementation of a high-speed iterative sorted MMSE QR decomposition, IEEE International Symposium on Circuits and Systems, New Orleans, LA, pp. 1421-1424.
-
Stewart, G.W., (1998) Matrix Algorithms: Basic Decompositions, vol. 1. Society for Industrial and Applied Mathematics, Philadelphia, PA.
-
Hadjidimos, A., (1978) Accelerated overrelaxation method, Mathematics of Computation,32(141), pp. 149-157.
-
Dutta, D., (2020) Numerical Optimization in Engineering and Sciences, Select Proceedings of NOIEAS 2019, Springer Nature.
-
Youssef, I.K. and Taha, A., (2013) On the modified successive overrelaxation method, Applied Mathematics and Computation, 219(9), pp. 4601-4613.
-
Varga, R.S., (1962) Iterative Analysis,Springer.
-
Tsai, P.C.-Y., Lee, K.K.-C., and Chen., C.-E., (2018) An Eigen-based Matrix Inverse Approximation Scheme with Stair Matrix Splitting for Massive MIMO Systems, International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Ishigaki, Okinawa, Japan, pp. 378-381.