Low-Complexity Iterative Detection for Uplink Multiuser Large-Scale MIMO
محورهای موضوعی : Wireless Network
Mojtaba Amiri
1
(Shahed University)
Mahmoud Ferdosizade Naeiny
2
(Shahed University)
کلید واژه: Massive MIMO, , Iterative Method, , Matrix Inversion, , Maximum Likelihood, , MMSE Detection, ,
چکیده مقاله :
In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative method is proposed for detection of the transmitted symbols in uplink multiuser massive MIMO systems. Linear detection algorithms such as minimum-mean-square-error (MMSE) and zero-forcing (ZF), are able to achieve the performance of the near optimal detector, when the number of base station (BS) antennas is enough high. But the complexity of linear detectors in Massive MIMO systems is high due to the necessity of the calculation of the inverse of a large dimension matrix. In this paper, we address the problem of reducing the complexity of the MMSE detector for massive MIMO systems. The proposed method is based on Gram Schmidt algorithm, which improves the convergence speed and also provides better error rate than the alternative methods. It will be shown that the complexity order is reduced from O(〖n_t〗^3) to O(〖n_t〗^2), where n_t is the number of users. The proposed method avoids the direct computation of matrix inversion. Simulation results show that the proposed method improves the convergence speed and also it achieves the performance of MMSE detector with considerable lower computational complexity.
In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative method is proposed for detection of the transmitted symbols in uplink multiuser massive MIMO systems. Linear detection algorithms such as minimum-mean-square-error (MMSE) and zero-forcing (ZF), are able to achieve the performance of the near optimal detector, when the number of base station (BS) antennas is enough high. But the complexity of linear detectors in Massive MIMO systems is high due to the necessity of the calculation of the inverse of a large dimension matrix. In this paper, we address the problem of reducing the complexity of the MMSE detector for massive MIMO systems. The proposed method is based on Gram Schmidt algorithm, which improves the convergence speed and also provides better error rate than the alternative methods. It will be shown that the complexity order is reduced from O(〖n_t〗^3) to O(〖n_t〗^2), where n_t is the number of users. The proposed method avoids the direct computation of matrix inversion. Simulation results show that the proposed method improves the convergence speed and also it achieves the performance of MMSE detector with considerable lower computational complexity.
[1] J. Thompson, X. Ge, H.-C. Wu, R. Irmer, H. Jiang, G. Fettweis, et al., "5G wireless communication systems: Prospects and challenges," IEEE Communications Magazine, vol. 52, pp. 62-64, 2014.
[2] E. Björnson, E. G. Larsson, and T. L. Marzetta, "Massive MIMO: Ten myths and one critical question," IEEE Communications Magazine, vol. 54, pp. 114-123, 2016.
[3] M. A. Albreem, M. Juntti, and S. Shahabuddin, "Massive MIMO detection techniques: a survey," IEEE Communications Surveys & Tutorials, vol. 21, pp. 3109-3132, 2019.
[4] P. Zhang, X. Yang, J. Chen, and Y. Huang, "A survey of testing for 5G: Solutions, opportunities, and challenges," China Communications, vol. 16, pp. 69-85, 2019.
[5] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. Soong, et al., "What will 5G be?," IEEE Journal on Selected areas in Communications, vol. 32, pp. 1065-1082, 2014.
[6] Y. Arjoune and S. Faruque, "Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction," in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 1023-1028.
[7] E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, "Massive MIMO for next generation wireless systems," IEEE Communications Magazine, vol. 52, pp. 186-195, 2014.
[8] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, et al., "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Processing Magazine, vol. 30, pp. 40-60, 2013.
[9] M. O. Damen, H. El Gamal, and G. Caire, "On maximum-likelihood detection and the search for the closest lattice point," IEEE Transactions on Information Theory, vol. 49, pp. 2389-2402, 2003.
[10] C. Thrampoulidis, W. Xu, and B. Hassibi, "Symbol error rate performance of box-relaxation decoders in massive MIMO," IEEE Transactions on Signal Processing, vol. 66, pp. 3377-3392, 2018.
[11] E. G. Larsson and J. Jalden, "Fixed-complexity soft MIMO detection via partial marginalization," IEEE transactions on Signal Processing, vol. 56, pp. 3397-3407, 2008.
[12] Z. Guo and P. Nilsson, "Algorithm and implementation of the K-best sphere decoding for MIMO detection," IEEE Journal on Selected areas in Communications, vol. 24, pp. 491-503, 2006.
[13] N. A. Khan, S. Ahmed, D. M. S. Bhatti, M. Z. Tunio, and S. Kim, "Study of MIMO detection schemes for emerging wireless communications," International Journal of Computer Science and Network Security, vol. 18, pp. 142-149, 2018.
[14] Y. N. Ahmed, "Enhanced low-complexity matrix inversion method for Massive MIMO systems," in 2019 16th International Symposium on Wireless Communication Systems (ISWCS), 2019, pp. 495-499.
[15] C. Tang, Y. Tao, Y. Chen, C. Liu, L. Yuan, and Z. Xing, "Approximate iteration detection and precoding in Massive MIMO," China Communications, vol. 15, pp. 183-196, 2018.
[16] L. Dai, X. Gao, X. Su, S. Han, I. Chih-Lin, and Z. Wang, "Low-complexity soft-output signal detection based on Gauss–Seidel method for uplink multiuser large-scale MIMO systems," IEEE Transactions on Vehicular Technology, vol. 64, pp. 4839-4845, 2015.
[17] X. Qin, Z. Yan, and G. He, "A near-optimal detection scheme based on joint steepest descent and Jacobi method for uplink Massive MIMO systems," IEEE Communications Letters, vol. 20, pp. 276-279, 2016.
[18] G. Peng, L. Liu, P. Zhang, S. Yin, and S. Wei, "Low-computing-load, high-parallelism detection method based on Chebyshev iteration for Massive MIMO systems with VLSI architecture," IEEE Transactions on Signal Processing, vol. 65, pp. 3775-3788, 2017.
[19] F. Jin, Q. Liu, H. Liu, and P. Wu, "A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems," IEEE Communications Letters, vol. 23, pp. 748-751, 2019.
[20] M. Wu, B. Yin, G. Wang, C. Dick, J. R. Cavallaro, and C. Studer, "Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations," IEEE Journal of Selected Topics in Signal Processing, vol. 8, pp. 916-929, 2014.
[21] D. Zhu, B. Li, and P. Liang, "On the matrix inversion approximation based on Neumann series in massive MIMO systems," in 2015 IEEE International Conference on Communications (ICC), 2015, pp. 1763-1769.
[22] X. Liu, Z. Zhang, X. Wang, J. Lian, and X. Dai, "A Low complexity high performance weighted Neumann series-based Massive MIMO detection," in 2019 28th Wireless and Optical Communications Conference (WOCC), 2019, pp. 1-5.
[23] M. A. Albreem, "Approximate matrix inversion methods for massive mimo detectors," in 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), 2019, pp. 87-92.
[24] M. A. Albreem, A. A. El-Saleh, and M. Juntti, "Linear Massive MIMO uplink detector based On joint jacobi and Gauss-Seidel methods," in 2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020, pp. 1-4.
[25] M. Burger, B. Kaltenbacher, and A. Neubauer, "Iterative solution methods," in Handbook of Mathematical Methods in Imaging, ed: Springer, 2011, pp. 345-384.
[26] R. I. Hernández, R. Baghaie, and K. Kettunen, "Implementation of Gram-Schmidt conjugate direction and conjugate gradient algorithms," in Proc. IEEE Finish Signal Processing Symposium, 1999, pp. 165-169.
[27] J. R. Shewchuk, "An introduction to the conjugate gradient method without the agonizing pain," ed: Carnegie-Mellon University. Department of Computer Science, 1994.
