An Efficient Estimation of the Number of Optimal Iterations for GS Pre-coding in Downlink Massive MIMO Systems
Source abstract
Abstract
This paper proposes an estimation scheme of the number iterations for optimal Gauss–Seidel (GS) pre-coding in the downlink massive multiple input multiple output (MIMO) systems for the first time. The number of iterations in GS pre-coding is one of the key parameters and should be estimated accurately prior to signal transmission in the downlink systems. For efficient estimation without presentations of the closed-form solution for the GS pre-coding symbols, the proposed estimation scheme uses the relative method which calculates the normalized Euclidean distance (NED) between consecutive GS solutions by using the property of the monotonic decrease function of the GS solutions. Additionally, an efficient initial solution for the GS pre-coding is proposed as a two term Neumann series (NS) based on the stair matrix for improving the accuracy of estimation and accelerating the convergence rate of the GS solution. The evaluated estimation performances verify high accuracy in the downlink massive MIMO systems even in low loading factors. In addition, an additional complexity for estimating the number of the optimal iterations is nearly negligible.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced MIMO Systems Optimization · Advanced Wireless Communication Techniques · Cooperative Communication and Network Coding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea
Department of Computer Engineering, Sejong University, Seoul 05006, Korea
References (30)
Show source-provided references
1. Marzetta, T.L. (2016). Fundamentals of Massive MIMO, Cambridge University Press.10.1017/CBO9781316799895 ↗
2. Björnson, E., Hoydis, J., and Sanguinetti, L. (2017). Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency. Found. Trends Signal Process., 11.10.1561/2000000093 ↗
3. Scaling up MIMO: Opportunities and challenges with very large arrays10.1109/MSP.2011.2178495 ↗
4. Massive MIMO for next generation wireless systems10.1109/MCOM.2014.6736761 ↗
5. Massive MIMO two-hop relay systems over Rician fading channels
6. Performance analysis of MRT-based dual-polarized massive MIMO system with space-polarization division multiple access
7. Massive MIMO with transceiver hardware impairments: Performance analysis and phase noise error minimization
8. Pilot sequence assignment for spatially correlated massive MIMO circumstances
9. Deep CNN based pilot allocation scheme in massive MIMO systems
10. Massive MIMO linear precoding: A survey10.1109/JSYST.2017.2776401 ↗
11. Massive MIMO detection techniques: A survey10.1109/COMST.2019.2935810 ↗
12. Albreem, M.A., El-Saleh, A.A., and Juntti, M. (2019, January 15–18). On approximate matrix inversion methods for massive MIMO detectors. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco.10.1109/WCNC.2019.8885673 ↗
13. Zhang, Z., Wu, J., Ma, X., Dong, Y., Wang, Y., Chen, S., and Dai, X. (2016, January 27–19). Reviews of recent progress on low-complexity linear detection via iterative algorithms for massive MIMO systems. Proceedings of the 2016 IEEE/CIC International Conference on Communications in China (ICCC Workshops), Chengdu, China.
14. Gao, X., Dai, L., Zhang, J., and Han, S.C.I. (2015, January 8–12). Capacity-approaching linear precoding with low-complexity for large-scale MIMO systems. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.10.1109/ICC.2015.7248549 ↗
15. Lee, Y., and Sou, S. (2018, January 27–30). On improving Gauss-Seidel iteration for signal detection in uplink multiuser massive MIMO systems. Proceedings of the 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, Japan.10.1109/CCOMS.2018.8463344 ↗
16. Minango, J., and de Almeida, C. (2017, January 8–10). Low-complexity MMSE detector based on refinement Gauss-Seidel method for massive MIMO systems. Proceedings of the 2017 IEEE 9th Latin-American Conference on Communications (LATINCOM), Guatemala City, Guatemala.10.1109/LATINCOM.2017.8240166 ↗
17. Stair matrix and its applications to massive MIMO uplink data detection10.1109/TCOMM.2017.2789211 ↗
18. Efficient initialization of iterative linear massive MIMO detectors using a stair matrix10.1049/el.2019.2938 ↗
19. Albreem, M.A., Alsharif, M.H., and Kim, S. (2020). Impact of stair and diagonal matrices in iterative linear massive MIMO uplink detectors for 5G wireless networks. Symmetry, 12.10.3390/sym12010071 ↗
20. Wu, Z., Zhang, C., Xue, Y., Xu, S., and You, X. (2016, January 22–25). Efficient architecture for soft-output massive MIMO detection with Gauss-Seidel method. Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada.10.1109/ISCAS.2016.7538940 ↗
21. Zhang, C., Wu, Z., Studer, C., Zhang, Z., and You, X. (2018). Efficient soft-output Gauss-Seidel data detector for massive MIMO systems. IEEE Trans. Circuits Syst. I Regul. Pap., 1–12.
22. Low-complexity soft-output signal detection based on Gauss–Seidel method for uplink multiuser large-scale MIMO systems10.1109/TVT.2014.2370106 ↗
23. Gao, X., Lu, Z., Han, Y., and Ning, J. (2014, January 25–27). Near-optimal signal detection with low complexity based on Gauss-Seidel method for uplink large-scale MIMO systems. Proceedings of the 2014 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, Beijing, China.10.1109/BMSB.2014.6873569 ↗
24. An improved Gauss-Seidel algorithm and its efficient architecture for massive MIMO systems
25. Wu, Z., Xue, Y., You, X., and Zhang, C. (2017, January 23–25). Hardware efficient detection for massive MIMO uplink with parallel Gauss-Seidel method. Proceedings of the 2017 22nd International Conference on Digital Signal. Processing (DSP), London, UK.10.1109/ICDSP.2017.8096142 ↗
26. Saad, Y. (2003). Iterative Methods for Sparse Linear Systems, SIAM.10.1137/1.9780898718003 ↗
27. Zhu, D., Li, B., and Liang, P. (2015, January 8–12). On the matrix inversion approximation based on Neumann series in massive MIMO systems. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.10.1109/ICC.2015.7248580 ↗
28. Prabhu, H., Rodrigues, J., Edfors, O., and Rusek, F. (2013, January 7–10). Approximative matrix inverse computations for very-large MIMO and applications to linear pre-coding systems. Proceedings of the 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China.10.1109/WCNC.2013.6554990 ↗
29. An efficient precoding method for improved downlink massive MIMO system10.1109/ACCESS.2019.2934090 ↗
30. Tulino, A.M., and Verdu, S. (2004). Random Matrix Theory and Wireless Communications, Now Publishers Inc.10.1561/9781933019505 ↗