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Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system

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Abstract

Acquisition of accurate channel state information (CSI) at transmitters results in a huge pilot overhead in massive multiple input multiple output (MIMO) systems due to the large number of antennas in the base station (BS). To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing (CS) theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit (SJMP) algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.

Key words

Compressed sensing Multi-user massive multiple input multiple output (MIMO) Frequency-division duplexing Structured joint channel estimation Pilot overhead reduction 

CLC number

TN911.5 

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References

  1. Barbotin, Y., Hormati, A., Rangan, S., et al., 2012. Estimation of sparse MIMO channels with common support. IEEE Trans. Commun., 60(12):3705–3716. https://doi.org/10.1109/TCOMM.2012.091112.110439CrossRefGoogle Scholar
  2. Baum, D.S., Hansen, J., Salo, J., 2005. An interim channel model for beyond-3G systems: extending the 3GPP spatial channel model (SCM). IEEE 61st Vehicular Technology Conf., p.3132–3136. https://doi.org/10.1109/VETECS.2005.1543924Google Scholar
  3. Berger, C.R., Wang, Z.H., Huang, J.Z., et al., 2010. Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag., 48(11):164–174. https://doi.org/10.1109/MCOM.2010.5621984CrossRefGoogle Scholar
  4. Björnson, E., Larsson, E.G., Marzetta, T.L., 2015. Massive MIMO: ten myths and one critical question. IEEE Commun. Mag., 54(2):114–123. https://doi.org/10.1109/MCOM.2016.7402270CrossRefGoogle Scholar
  5. Bogale, T.E., Vandendorpe, L., Chalise, B.K., 2012. Robust transceiver optimization for downlink coordinated base station systems: distributed algorithm. IEEE Trans. Signal Process., 60(1):337–350. https://doi.org/10.1109/TSP.2011.2170167MathSciNetCrossRefGoogle Scholar
  6. Chen, Y., Qin, Z., 2015. Gradient-based compressive image fusion. Front. Inform. Technol. Electron. Eng., 16(3):227–237. https://doi.org/10.1631/FITEE.1400217MathSciNetCrossRefGoogle Scholar
  7. Choi, J., Love, D.J., Bidigare, P., 2014. Downlink training techniques for FDD massive MIMO systems: open-loop and closed-loop training with memory. IEEE J. Sel. Top. Signal Process., 8(5):802–814. https://doi.org/10.1109/JSTSP.2014.2313020CrossRefGoogle Scholar
  8. Dai, L.L., Wang, J.T., Wang, Z.C., et al., 2013. Spectrum-and energy-efficient OFDM based on simultaneous multichannel reconstruction. IEEE Trans. Signal Process., 61(23):6047–6059. https://doi.org/10.1109/TSP.2013.2282920MathSciNetCrossRefGoogle Scholar
  9. Dai, W., Milenkovic, O., 2009. Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inform. Theory, 55(5):2230–2249. https://doi.org/10.1109/TIT.2009.2016006MathSciNetCrossRefGoogle Scholar
  10. Dasgupta, S., Gupta, A., 2003. An elementary proof of a theorem of Johnson and Lindenstrauss. Rand. Struct. Algor., 22(1):60–65. https://doi.org/10.1002/rsa.10073MathSciNetCrossRefGoogle Scholar
  11. Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289–1306. https://doi.org/10.1109/TIT.2006.871582MathSciNetCrossRefGoogle Scholar
  12. Eldar, Y.C., Kuppinger, P., Bölcskei, H., 2010. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans. Signal Process., 58(6):3042–3054. https://doi.org/10.1109/TSP.2010.2044837MathSciNetCrossRefGoogle Scholar
  13. Gao, X., Edfors, O., Rusek, F., et al., 2011. Linear pre-coding performance in measured very-large MIMO channels. IEEE Vehicular Technology Conf., p.1–5. https://doi.org/10.1109/VETECF.2011.6093291Google Scholar
  14. Gao, Z., Dai, L.L., Wang, Z., 2014. Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems. Electron. Lett., 50(12):896–898. https://doi.org/10.1049/el.2014.0985CrossRefGoogle Scholar
  15. Gao, Z., Dai, L.L., Wang, Z., et al., 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.2015.2463260MathSciNetCrossRefGoogle Scholar
  16. Gao, Z., Dai, L.L., Dai, W., et al., 2016. Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO. IEEE Trans. Commun., 64(2):601–617. https://doi.org/10.1109/TCOMM.2015.2508809CrossRefGoogle Scholar
  17. Hoydis, J., Hoek, C., Wild, T., et al., 2012. Channel measurements for large antenna arrays. IEEE Int. Symp. on Wireless Communication Systems, p.811–815. https://doi.org/10.1109/ISWCS.2012.6328480Google Scholar
  18. Hoydis, J., Ten Brink, S., Debbah, M., 2013. Massive MIMO in the UL/DL of cellular networks: how many antennas do we need? IEEE J. Sel. Areas Commun., 31(2):160–171. https://doi.org/10.1109/JSAC.2013.130205CrossRefGoogle Scholar
  19. Hu, D., Wang, X.D., He, L.H., 2013. A new sparse channel estimation and tracking method for time-varying OFDM systems. IEEE Trans. Veh. Technol., 62(9):4648–4653. https://doi.org/10.1109/TVT.2013.2266282CrossRefGoogle Scholar
  20. Ketonen, J., Juntti, M., Cavallaro, J.R., 2010. Performancecomplexity comparison of receivers for a LTE MIMOOFDM system. IEEE Trans. Signal Process., 58(6):3360–3372. https://doi.org/10.1109/TSP.2010.2044290MathSciNetCrossRefGoogle Scholar
  21. Lee, B., Choi, J., Seol, J.Y., et al., 2015. Antenna grouping based feedback compression for FDD-based massive MIMO systems. IEEE Trans. Commun., 63(9):3261–3274. https://doi.org/10.1109/TCOMM.2015.2460743CrossRefGoogle Scholar
  22. Lu, L., Li, G.Y., Swindlehurst, A.L., et al., 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.2014.2317671CrossRefGoogle Scholar
  23. Noh, S., Zoltowski, M.D., Sung, Y., et al., 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.2014.2327572CrossRefGoogle Scholar
  24. Qi, C.H., Wu, L.N., 2014. Uplink channel estimation for massive MIMO systems exploring joint channel sparsity. Electron. Lett., 50(23):1770–1772. https://doi.org/10.1049/iet-com.2013.0781CrossRefGoogle Scholar
  25. Rao, X.B., Lau, V.K.N., 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.2014.2324991MathSciNetCrossRefGoogle Scholar
  26. Tropp, J.A., Gilbert, A.C., 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory, 53(12):4655–4666. https://doi.org/10.1109/TIT.2007.909108MathSciNetCrossRefGoogle Scholar
  27. Tropp, J.A., Gilbert, A.C., Strauss, M.J., 2006. Algorithms for simultaneous sparse approximation. Part I: greedy pursuit. Signal Process., 86(3):572–588. https://doi.org/10.1016/j.sigpro.2005.05.030CrossRefGoogle Scholar
  28. Tse, D., Viswanath, P., 2005. Fundamentals of Wireless Communication. Cambridge University Press, New York, p.309–330.CrossRefGoogle Scholar
  29. Yin, H.F., Gesbert, D., Filippou, M., et al., 2012. A coordinated approach to channel estimation in large-scale multipleantenna systems. IEEE J. Sel. Areas Commun., 31(2):264–273. https://doi.org/10.1109/JSAC.2013.130214CrossRefGoogle Scholar
  30. Zhang, Z.Y., Teh, K.C., Li, K.H., 2014. Application of compressive sensing to limited feedback strategy in largescale multiple-input single-output cellular networks. IET Commun., 8(6):947–955. https://doi.org/10.1049/iet-com.2013.0781CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Communication Research CenterHarbin Institute of TechnologyHarbinChina

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