KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권4호
  • /
  • Pages.1504-1526
  • /
  • 2018
  • /
  • 1976-7277(pISSN)
  • /
  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
DOI QR코드

DOI QR Code

A comparative study of low-complexity MMSE signal detection for massive MIMO systems

  • Zhao, Shufeng (School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications) ;
  • Shen, Bin (School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications) ;
  • Hua, Quan (School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications)
  • 투고 : 2017.05.23
  • 심사 : 2017.12.04
  • 발행 : 2018.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

For uplink multi-user massive MIMO systems, conventional minimum mean square error (MMSE) linear detection method achieves near-optimal performance when the number of antennas at base station is much larger than that of the single-antenna users. However, MMSE detection involves complicated matrix inversion, thus making it cumbersome to be implemented cost-effectively and rapidly. In this paper, we first summarize in detail the state-of-the-art simplified MMSE detection algorithms that circumvent the complicated matrix inversion and hence reduce the computation complexity from ${\mathcal{O}}(K^3)$ to ${\mathcal{O}}(K^2)$ or ${\mathcal{O}}(NK)$ with some certain performance sacrifice. Meanwhile, we divide the simplified algorithms into two categories, namely the matrix inversion approximation and the classical iterative linear equation solving methods, and make comparisons between them in terms of detection performance and computation complexity. In order to further optimize the detection performance of the existing detection algorithms, we propose more proper solutions to set the initial values and relaxation parameters, and present a new way of reconstructing the exact effective noise variance to accelerate the convergence speed. Analysis and simulation results verify that with the help of proper initial values and parameters, the simplified matrix inversion based detection algorithms can achieve detection performance quite close to that of the ideal matrix inversion based MMSE algorithm with only a small number of series expansions or iterations.

키워드

참고문헌

  1. A. Lozano and N. Jindal, "Transmit diversity vs. spatial multiplexing in modern MIMO systems," IEEE Transactions on Wireless Communications, vol. 9, no. 1, pp. 186-197, January, 2010. https://doi.org/10.1109/TWC.2010.01.081381
  2. D. Gesbert, M. Kountouris, J. R. W. Heath, C. b. Chae and T. Salzer, "Shifting the MIMO Paradigm," IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 36-46, September, 2007. https://doi.org/10.1109/MSP.2007.904815
  3. X. Ge, R. Zi, H. Wang, J. Zhang and M. Jo, "Multi-User Massive MIMO Communication Systems Based on Irregular Antenna Arrays," IEEE Transactions on Wireless Communications, vol. 15, no.8, pp. 5287-5301, August, 2016. https://doi.org/10.1109/TWC.2016.2555911
  4. 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) Multiplexing and channel coding (Release 9), TS 36. 212 Rev. 8.3.0, 3GPP Organizational Partners, May, 2008.
  5. F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors and F. Tufvesson, "Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays," IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40-60, January, 2013. https://doi.org/10.1109/MSP.2011.2178495
  6. J. Jose, A. Ashikhmin, T. L. Marzetta and S. Vishwanath, "Pilot Contamination and Precoding in Multi-Cell TDD Systems," IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2640-2651, August, 2011. https://doi.org/10.1109/TWC.2011.060711.101155
  7. F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta and P. Popovski, "Five disruptive technology directions for 5G," IEEE Communications Magazine, vol. 52, no. 2, pp. 74-80, February, 2014. https://doi.org/10.1109/MCOM.2014.6736746
  8. M. Qian, Y. Wang, Y. Zhou and J. Shi, "A super base station based centralized network architecture for 5G mobile communication systems," Digital Communications and Networks vol. 1, no. 2, pp. 152-159, April, 2015. https://doi.org/10.1016/j.dcan.2015.02.003
  9. D.C. Araujo, T. Maksymyuk, et al, "Massive MIMO: Survey and Future Research Topics," IET Communications, vol. 10, no.15, pp. 1938-1946, October, 2016. https://doi.org/10.1049/iet-com.2015.1091
  10. N. Srinidhi, T. Datta, A. Chockalingam and B. S. Rajan, "Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance," IEEE Transactions on Communications, vol. 59, no. 11, pp. 2955-2963, November, 2011. https://doi.org/10.1109/TCOMM.2011.070511.110058
  11. L. G. Barbero and J. S. Thompson, "Fixing the Complexity of the Sphere Decoder for MIMO Detection," IEEE Transactions on Wireless Communications, vol. 7, no. 6, pp. 2131-2142, June, 2008. https://doi.org/10.1109/TWC.2008.060378
  12. J. Goldberger and A. Leshem, "MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation," IEEE Transactions on Information Theory, vol. 57, no. 8, pp. 4973-4982, August, 2011. https://doi.org/10.1109/TIT.2011.2159037
  13. 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, no. 5, pp. 916-929, October, 2014. https://doi.org/10.1109/JSTSP.2014.2313021
  14. B. Kang, J. H. Yoon and J. Park, "Low complexity massive MIMO detection architecture based on Neumann method," in Proc. of 2015 International SoC Design Conference (ISOCC) pp. 293-294, November 2-5, 2015.
  15. T. Li, S. Patole and M. Torlak, "A multistage linear receiver approach for MMSE detection in massive MIMO," in Proc. of 2014 48th Asilomar Conference on Signals, Systems and Computers, pp. 2067-2072, November 2-5, 2014.
  16. C. Tang, C. Liu, L. Yuan and Z. Xing, "High Precision Low Complexity Matrix Inversion Based on Newton Iteration for Data Detection in the Massive MIMO," IEEE Communications Letters, vol. 20, no. 3, pp. 490-493, March, 2016. https://doi.org/10.1109/LCOMM.2015.2514281
  17. X. Gao, L. Dai, C. Yuen and Y. Zhang, "Low-complexity MMSE signal detection based on Richardson method for large-scale MIMO systems," in Proc. of 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), pp. 1-5, Stepmother 14-17, 2014.
  18. X. Gao, L. Dai, Y. Hu, Z. Wang and Z. Wang, "Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems," in Proc. of 2014 IEEE Global Communications Conference, pp. 3291-3295, December 8-12, 2014.
  19. J. Ning, Z. Lu, T. Xie and J. Quan, "Low complexity signal detector based on SSOR method for massive MIMO systems," in Proc. of 2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-4, June 17-19, 2015.
  20. Y. Hu, Z. Wang, X. Gaol and J. Ning, "Low-complexity signal detection using CG method for uplink large-scale MIMO systems," in Proc. of 2014 IEEE International Conference on Communication Systems, pp. 477-481, November 19-21, 2014.
  21. S. Wu, L. Kuang, Z. Ni, J. Lu, D. Huang and Q. Guo, "Low-Complexity Iterative Detection for Large-Scale Multiuser MIMO-OFDM Systems Using Approximate Message Passing," IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 902-915, October, 2014. https://doi.org/10.1109/JSTSP.2014.2313766
  22. D. L. Donoho, A. Maleki and A. Montanari, "Message passing algorithms for compressed sensing: I. motivation and construction," in Proc. of 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo), pp. 1-5, January 6-8, 2010.
  23. M. Bayati and A. Montanari, "The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing," IEEE Transactions on Information Theory, vol. 57, no. 2, pp. 764-785, February, 2011. https://doi.org/10.1109/TIT.2010.2094817
  24. B. Ren, Y. Wang, S. Sun, Y. Zhang, X. Dai and K. Niu, "Low-complexity MMSE-IRC algorithm for uplink massive MIMO systems," Electronics Letters, vol. 53, no. 14, pp. 972-974, July, 2017. https://doi.org/10.1049/el.2017.1133
  25. L. Dai, X. Gao, X. Su, S. Han, I. C. L 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, no. 10, pp. 4839-4845, October, 2015. https://doi.org/10.1109/TVT.2014.2370106
  26. L. Dai, Z. Wang and Z. Yang, "Time-Frequency Training OFDM with High Spectral Efficiency and Reliable Performance in High Speed Environments," IEEE Journal on Selected Areas in Communications, vol. 30, no. 4, pp. 695-707, May, 2012. https://doi.org/10.1109/JSAC.2012.120504
  27. Bjorck, Ake. Numerical methods in matrix computations. New York: Springer, 2015.
  28. T. L. Narasimhan and A. Chockalingam, "Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems," IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 847-860, October, 2014. https://doi.org/10.1109/JSTSP.2014.2314213
  29. A. Maleki, L. Anitori, Z. Yang and R. G. Baraniuk, "Asymptotic Analysis of Complex LASSO via Complex Approximate Message Passing (CAMP)," IEEE Transactions on Information Theory, vol. 59, no. 7, pp. 4290-4308, July, 2013. https://doi.org/10.1109/TIT.2013.2252232
  30. D. Guo, Y. Wu, S. S. Shitz and S. Verdu, "Estimation in Gaussian Noise: Properties of the Minimum Mean-Square Error," IEEE Transactions on Information Theory, vol. 57, no. 4, pp. 2371-2385, April, 2011. https://doi.org/10.1109/TIT.2011.2111010