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Low-Complexity Massive MIMO Detectors Based on Richardson Method

  • Kang, Byunggi (School of Electrical Engineering, Korea University) ;
  • Yoon, Ji-Hwan (School of Electrical Engineering, Korea University) ;
  • Park, Jongsun (School of Electrical Engineering, Korea University)
  • Received : 2016.10.14
  • Accepted : 2017.01.12
  • Published : 2017.06.01

Abstract

In the uplink transmission of massive (or large-scale) multi-input multi-output (MIMO) systems, large dimensional signal detection and its hardware design are challenging issues owing to the high computational complexity. In this paper, we propose low-complexity hardware architectures of Richardson iterative method-based massive MIMO detectors. We present two types of massive MIMO detectors, directly mapped (type1) and reformulated (type2) Richardson iterative methods. In the proposed Richardson method (type2), the matrix-by-matrix multiplications are reformulated to matrix-vector multiplications, thus reducing the computational complexity from $O(U^2)$ to O(U). Both massive MIMO detectors are implemented using a 65 nm CMOS process and compared in terms of detection performance under different channel conditions (high-mobility and flat fading channels). The hardware implementation results confirm that the proposed type1 Richardson method-based detector demonstrates up to 50% power savings over the proposed type2 detector under a flat fading channel. The type2 detector indicates a 37% power savings compared to the type1 under a high-mobility channel.

Keywords

References

  1. H. Sampath et al., "A Fourth-Generation MIMO-OFDM Broadband Wireless System: Design, Performance, and Field Trial Results," IEEE Commun. Mag., vol. 40, no. 9, Sept. 2002, pp. 143-149. https://doi.org/10.1109/MCOM.2002.1031841
  2. S. Sesia, I. Toufik, and M. Baker, "Introduction and Background," LTE, the UMTS Long Term Evolution: From Theory to Practice, 1st ed, New York, USA: Wiley, 2009, pp. 1-21.
  3. 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (EUTRA); Physical Layer Procedures (Release 10), TS 36.213 version 10.10.0, July 2013.
  4. IEEE Draft Standard Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Amendment 4: Enhancements for Higher Throughput, P802.11n D3.00, Sept. 2007.
  5. K. Zheng et al., "Survey of Large-Scale MIMO Systems," IEEE Commun. Surveys Tutorials, vol. 17, no. 3, 2015, pp. 1738-1760. https://doi.org/10.1109/COMST.2015.2425294
  6. F. Rusek et al., "Scaling up MIMO: Opportunities and Challenges with Very Large Arrays," IEEE Signal Process. Mag., vol. 30, no. 1, Jan. 2013, pp. 40-60. https://doi.org/10.1109/MSP.2011.2178495
  7. M. Wu et al., "Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, Oct. 2014, pp. 916-929. https://doi.org/10.1109/JSTSP.2014.2313021
  8. 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 Commun. Lett., vol. 20, no. 2, Feb. 2016, pp. 276-279. https://doi.org/10.1109/LCOMM.2015.2504506
  9. T.L. Marzetta, "Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas," IEEE Trans. Wireless Commun., vol. 9, no. 11, Nov. 2010, pp. 3590-3600. https://doi.org/10.1109/TWC.2010.092810.091092
  10. M. Wu et al., "Approximate Matrix Inversion for High-Throughput Data Detection in the Large-Scale MIMO Uplink," IEEE Int. Symp. Circuits Syst., Beijing, China, May 19-23, 2013, pp. 2155-2158.
  11. X. Gao et al., "Low-Complexity Near-Optimal Signal Detection for Uplink Large-Scale MIMO Systems," IET Electron. Lett., vol. 50, no. 18, Aug. 2014, pp. 1326-1328. https://doi.org/10.1049/el.2014.0713
  12. X. Gao et al., "Low-Complexity MMSE Signal Detection Based on Richardson Method for Large-Scale MIMO Systems," IEEE Veh. Technol. Conf., Seoul, Rep. of Korea, Sept. 14-17, 2014, pp. 1-5.
  13. X. Gao et al., "Matrix Inversion-Less Signal Detection Using SOR Method for Uplink Large-Scale MIMO Systems," IEEE Global Commun. Conf., Austin, TX, USA, Dec. 8-12, 2014, pp. 3291-3295.
  14. L. Dai et al., "Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multiuser Large-Scale MIMO Systems," IEEE Trans. Veh. Technol., vol. 64, no. 10, Oct. 2015, pp. 4839-4845. https://doi.org/10.1109/TVT.2014.2370106
  15. J. Zhou, Y. Ye, and J. Hu, "Biased MMSE Soft-Output Detection Based on Jacobi Method in Massive MIMO," IEEE Int. Conf. Commun. Problem-Solving, Beijing, China, Dec. 5-7, 2014, pp. 442-445.
  16. B. Yin et al., "Conjugate Gradient-Based Soft-Output Detection and Precoding in Massive MIMO Systems," IEEE Global Commun. Conf., Austin, TX, USA, Dec. 8-12, 2014, pp. 3696-3701.
  17. B. Yin et al., "VLSI Design of Large-Scale Soft-Output MIMO Detection Using Conjugate Gradients," IEEE Int. Symp. Circuits Syst., Melbourne, Australia, May 24-27, 2014, pp. 1498-1501.
  18. J. Lin et al., "Low-Complexity High-Throughput QR Decomposition Design for MIMO Systems," IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 23, no. 10, Oct. 2015, pp. 2342-2346. https://doi.org/10.1109/TVLSI.2014.2361906
  19. I. Park and S. Kang, "Scheduling Algorithm for Partially Parallel Architecture of LDPC Decoder by Matrix Permutation," IEEE Int. Symp. Circuits Syst., Kobe, Japan, May 23-26, 2005, pp. 5778-5781.
  20. B. Yin et al., "A 3.8 Gb/s Large-Scale MIMO Detector for 3GPP LTE-Advanced," 2014 IEEE Int. Conf. Acoustics, Speech Signal Process., Florence, Italy, May 4-9, 2014, pp. 3879-3883.
  21. M.S. Khairy et al., "Algorithms and Architectures of Energy-Efficient Error-Resilient MIMO Detectors for Memory-Dominated Wireless Communication Systems," IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 61, no. 7, July 2014, pp. 2159-2171. https://doi.org/10.1109/TCSI.2014.2298273
  22. C. Liao, T. Wang, and T. Chiueh, "A 74.8 mW Soft-Output Detector IC for 8 $\times$ 8 Spatial-Multiplexing MIMO Communications," IEEE J. Solid-State Circuits, vol. 45, no. 2, Feb. 2010, pp. 411-421. https://doi.org/10.1109/JSSC.2009.2037292
  23. D. Yue, Y. Zhang, and Y. Jia, "Large-Scale SIMO Systems Based on Specular Component in Correlated Rician Fading Environments," Int. Conf. Instrum. Meas., Comput., Commun. Contr., Harbin, China, Sept. 18-20, 2014, pp. 840-844.
  24. Synopsys PrimeTime User's Manual. http://www.synopsys.com
  25. T. Zijian et al., "Pilot-Assisted Timevarying Channel Estimation for OFDM Systems," IEEE Trans. Signal Process., vol. 55, no. 5, May 2007, pp. 2226-2238. https://doi.org/10.1109/TSP.2007.893198
  26. C. Wang et al., "Cellular Architecture and Key Technologies for 5G Wireless Communication Networks," IEEE Commun. Mag., vol. 52, no. 2, Feb. 2014, pp. 122-130. https://doi.org/10.1109/MCOM.2014.6736752

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