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FDD massive MIMO downlink channel estimation with complex hybrid generalized approximate message passing algorithm

  • Wenyuan WangEmail author
  • Yue Xiu
  • Zhongpei Zhang
Original Research

Abstract

Precise channel state information (CSI) is essential for the massive multiple-input multiple-output (MIMO) system to achieve high spectrum and energy efficiency performance in the forthcoming 5G communication. Combined with angular domain channel sparsity, compressive sensing (CS) technique is introduced to estimate downlink CSI because it can help the frequency division duplex massive MIMO system to overcome the restriction of the limited pilot overhead. Conventional CS techniques consider different entries of the sparse signal as equivalent random variables. However, some scatters around the base station are fixed during practical propagation. Consequently, some propagation beams are more likely to locate within certain angular spread and the corresponding entries of the angular domain channel response vector are more likely to be non-zero valued. While this non-zero probability of a certain entry can be acquired offline by learning and analyzing the historical CSI, it is unnecessary to be estimated again during the sparse reconstruction process. When we describe the non-zero probability of a certain entry with the probabilistic density manner, hybrid prior probabilistic settings are used because of the practical propagation property. Combined with the complex generalized approximated message passing (GAMP) algorithm, a new channel estimation method is introduced in this paper. We define the GAMP algorithm with its intrinsic architecture and amended hybrid probability node settings as hybrid GAMP algorithm. A definite improvement of the pilot consumption as well as the estimation accuracy are simultaneously achieved through our proposed channel estimation method with complex hybrid GAMP algorithm and accurate hybrid settings. By further simulation, the influence of the inaccurate hybrid settings of the sparse channel response vector is drawn that the negative effect is proved to be quite small for the proposed channel estimation method.

Keywords

Channel estimation Complex Hybrid GAMP 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61571003 and 61671128.

References

  1. Abramson NM, Braverman DJ, Sebestyen GS (1963) Pattern recognition and machine learning. IEEE Trans Inf Theory 9(4):257–261.  https://doi.org/10.1109/TIT.1963.1057854 CrossRefGoogle Scholar
  2. Andersen MR (2014) Sparse inference using approximate message passingGoogle Scholar
  3. Angelosante D, Biglieri E, Lops M (2009) Sequential estimation of multipath MIMO-OFDM channels. IEEE Trans Signal Process 57(8):3167–3181.  https://doi.org/10.1109/TSP.2009.2020049 MathSciNetCrossRefzbMATHGoogle Scholar
  4. Bayati M, Montanari A (2010) The dynamics of message passing on dense graphs, with applications to compressed sensing. In: International symposium on information theory, pp 1528–1532.  https://doi.org/10.1109/ISIT.2010.5513529
  5. Bishop CM (2006) Pattern recognition and machine learning. J Electron ImagingGoogle Scholar
  6. Cheng P, Chen Z (2014). Multidimensional compressive sensing based analog CSI feedback for massive MIMO-OFDM Systems. Veh Technol Conf.  https://doi.org/10.1109/VTCFall.2014.6966062 Google Scholar
  7. Dai L, Wang Z, Yang Z (2013) Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems. IEEE J Sel Areas Commun 31(2):251–263.  https://doi.org/10.1109/JSAC.2013.130213 CrossRefGoogle Scholar
  8. Donoho DL, Maleki A, Montanari A (2009) Message-passing algorithms for compressed sensing. Proc Natl Acad Sci USA 106(45):18914–18919.  https://doi.org/10.1073/pnas.0909892106 CrossRefGoogle Scholar
  9. Fleury BH, Tschudin M, Heddergott R, Dahlhaus D, Pedersen KI (1999) Channel parameter estimation in mobile radio environments using the SAGE algorithm. IEEE J Sel Areas Commun 17(3):434–450.  https://doi.org/10.1109/49.753729 CrossRefGoogle Scholar
  10. Gao Z, Dai L, Wang Z, Chen S (2015) Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Trans Signal Process 63(23):6169–6183.  https://doi.org/10.1109/TSP.2015.2463260 MathSciNetCrossRefzbMATHGoogle Scholar
  11. Kuo P, Kung HT, Ting P (2012). Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays. Wirel Commun Netw Conf.  https://doi.org/10.1109/WCNC.2012.6214417 Google Scholar
  12. Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2):186–195.  https://doi.org/10.1109/MCOM.2014.6736761 CrossRefGoogle Scholar
  13. Lu L, Li GY, Swindlehurst AL, Ashikhmin AE, Zhang R (2014) An overview of massive MIMO: benefits and challenges. IEEE J Sel Top Signal Process 8(5):742–758.  https://doi.org/10.1109/JSTSP.2014.2317671 CrossRefGoogle Scholar
  14. Luo F-L, Zhang CJ (2016) Signal processing for 5G: algorithms and implementations.  https://doi.org/10.1002/9781119116493
  15. Maleki A, Montanari A (2010). Analysis of approximate message passing algorithm. Conf Inf Sci Syst.  https://doi.org/10.1109/CISS.2010.5464887 Google Scholar
  16. Maleki A, Anitori L, Yang Z, Baraniuk RG (2013) Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP). IEEE Trans Inf Theory 59(7):4290–4308.  https://doi.org/10.1109/TIT.2013.2252232 MathSciNetCrossRefzbMATHGoogle Scholar
  17. Noh S, Zoltowski MD, Sung Y, Love DJ (2014) Pilot beam pattern design for channel estimation in massive MIMO systems. IEEE J Sel Top Signal Process 8(5):787–801.  https://doi.org/10.1109/JSTSP.2014.2327572 CrossRefGoogle Scholar
  18. Obara T, Suyama S, Shen J, Okumura Y (2014) Joint fixed beamforming and eigenmode precoding for super high bit rate massive MIMO systems using higher frequency bands. Pers Indoor Mob Radio Commun.  https://doi.org/10.1109/PIMRC.2014.7136237 Google Scholar
  19. Osseiran A, Monserrat JF, Marsch P (2016) 5G mobile and wireless communications stechnology.  https://doi.org/10.1017/CBO9781316417744
  20. Rangan S (2011) Generalized approximate message passing for estimation with random linear mixing. In: International symposium on information theory, pp 2168–2172.  https://doi.org/10.1109/ISIT.2011.6033942
  21. Rangan S, Fletcher AK, Goyal VK, Schniter P (2012) Hybrid generalized approximate message passing with applications to structured sparsity. In: International symposium on information theory.  https://doi.org/10.1109/ISIT.2012.6283054
  22. Rao X, Lau VK (2014) Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems. IEEE Trans Signal Process 62(12):3261–3271.  https://doi.org/10.1109/TSP.2014.2324991 MathSciNetCrossRefzbMATHGoogle Scholar
  23. Sangodoyin S, Kristem V, Bas CU, Kaske M, Lee J, Schneider C, Molisch AF (2015) Cluster-based analysis of 3D MIMO channel measurement in an urban environment. Mil Commun Conf.  https://doi.org/10.1109/MILCOM.2015.7357533 Google Scholar
  24. Saxena V, Fodor G, Karipidis E (2015). Mitigating pilot contamination by pilot reuse and power control schemes for massive MIMO systems. Veh Technol Conf.  https://doi.org/10.1109/VTCSpring.2015.7145932 Google Scholar
  25. Shim S, Kwak JS, Heath RW, Andrews JG (2008) Block diagonalization for multi-user MIMO with other-cell interference. IEEE Trans Wirel Commun 7(7):2671–2681.  https://doi.org/10.1109/TWC.2008.070093 CrossRefGoogle Scholar
  26. Simko M, Diniz PS, Wang Q, Rupp M (2013) Adaptive pilot-symbol patterns for MIMO OFDM systems. IEEE Trans Wirel Commun 12(9):4705–4715.  https://doi.org/10.1109/TWC.2013.081413.121998 CrossRefGoogle Scholar
  27. Vila JP, Schniter P (2013) Expectation-maximization gaussian-mixture approximate message passing. IEEE Trans Signal Process 61(19):4658–4672.  https://doi.org/10.1109/TSP.2013.2272287 MathSciNetCrossRefzbMATHGoogle Scholar
  28. You L, Gao X, Swindlehurst AL, Zhong W (2015a) Adjustable phase shift pilots for sparse massive MIMO-OFDM channels. In: International workshop on signal processing advances in wireless communications.  https://doi.org/10.1109/SPAWC.2015.7227029
  29. You L, Gao X, Xia X, Ma N, Peng Y (2015b) Pilot reuse for massive MIMO transmission over spatially correlated rayleigh fading channels. IEEE Trans Wirel Commun 14(6):3352–3366.  https://doi.org/10.1109/TWC.2015.2404839 CrossRefGoogle Scholar
  30. You L, Gao X, Swindlehurst AL, Zhong W (2016) Channel acquisition for massive MIMO-OFDM with adjustable phase shift pilots. IEEE Trans Signal Process 64(6):1461–1476.  https://doi.org/10.1109/TSP.2015.2502550 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina

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