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CSI Transactions on ICT

, Volume 7, Issue 1, pp 13–26 | Cite as

Massive MIMO multi-pair two-way half-duplex AF FDD relaying: channel estimation

  • Dheeraj Naidu AmudalaEmail author
  • Anupama Rajoriya
  • Ekant Sharma
  • Sauradeep Dey
  • Rohit Budhiraja
S.I. : Wireless in the future
  • 80 Downloads

Abstract

We consider two-way amplify-and-forward FDD massive multi-input-multi-output (MIMO) relaying system, where multiple half-duplex (HD) user-pairs exchange information via a shared HD relay equipped with massive MIMO. To design and study the performance of these systems, we require channel state information in uplink as well as in downlink. For the uplink of the system model, we use conventional low-complexity MMSE estimator for the channel estimation, wherein the number of orthogonal pilots required for uplink channel estimation are of the order of the number of users. However, in the massive MIMO relay systems, the downlink channel estimation becomes challenging, because of increased pilot overhead in proportion to the number of antennas at the relay and employing conventional channel estimation approaches will result in poor spectral efficiency. The massive MIMO channels exhibits sparse structure in the angular domain, due to limited scattering environment. We exploit this channel sparsity to reduce the pilot overhead by performing sparse Bayesian learning-based downlink channel estimation. Furthermore, practical massive MIMO systems are built with low-cost hardware components which makes the massive MIMO system prone to hardware impairments like phase and quantization errors. In this paper, we numerically analyze the effect of hardware impairments on the two-way multi-pair half-duplex massive MIMO relay systems.

Keywords

Massive MIMO Spectral efficiency Two-way relay Half duplex Frequency division duplexing Downlink channel estimation Sparse Bayesian learning 

References

  1. 1.
    Lee K, Hanzo L (2010) Resource-efficient wireless relaying protocols. IEEE Wirel Commun 17(2):66–72CrossRefGoogle Scholar
  2. 2.
    Cui H, Song L, Jiao B (2014) Multi-pair two-way amplify-and-forward relaying with very large number of relay antennas. IEEE Trans Wirel Commun 13(5):2636–2645CrossRefGoogle Scholar
  3. 3.
    Cui H, Song L, Jiao B (2014) Multi-pair two-way amplify-and-forward relaying with very large number of relay antennas. IEEE Trans Wirel Commun 13(5):2636–2645CrossRefGoogle Scholar
  4. 4.
    Dai Y, Dong X (2016) Power allocation for multi-pair massive MIMO two-way AF relaying with linear processing. IEEE Trans Wirel Commun 15(9):5932–5946CrossRefGoogle Scholar
  5. 5.
    Björnson E, Matthaiou M, Debbah M (2015) Massive MIMO with non-ideal arbitrary arrays: hardware scaling laws and circuit-aware design. IEEE Trans Wirel Commun 14(8):4353–4368CrossRefGoogle Scholar
  6. 6.
    Schenk T (2008) RF imperfections in high-rate wireless systems: impact and digital compensation. Springer, DordrechtCrossRefGoogle Scholar
  7. 7.
    Zhang Q, Quek TQ, Jin S (2018) Scaling analysis for massive MIMO systems with hardware impairments in Rician fading. IEEE Trans Wirel Commun 17:4536–4549CrossRefGoogle Scholar
  8. 8.
    Zhang J, Xue X, Björnson E, Ai B, Jin S (2018) Spectral efficiency of multipair massive MIMO two-way relaying with hardware impairments. IEEE Wirel Commun Lett 7(1):14–17CrossRefGoogle Scholar
  9. 9.
    Bajwa WU, Haupt J, Sayeed AM, Nowak R (2010) Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc IEEE 98(6):1058–1076CrossRefGoogle Scholar
  10. 10.
    Chan PWC, Lo ES, Wang RR, Au EKS, Lau VKN, Cheng RS, Mow WH, Murch RD, Letaief KB (2006) The evolution path of 4G networks: FDD or TDD? IEEE Commun Mag 44(12):42–50CrossRefGoogle Scholar
  11. 11.
    Dutta B, Budhiraja R, Koilpillai R, Hanzo L (2018) Analysis of quantized MRC-MRT precoder for FDD massive MIMO two-way AF relaying. IEEE Trans Commun 67(2):988–1003CrossRefGoogle Scholar
  12. 12.
    Gao X, Tufvesson F, Edfors O (2013) Massive MIMO channels—measurements and models. In: 2013 Asilomar conference on signals, systems and computers, pp 280–284Google Scholar
  13. 13.
    Heath RW, González-Prelcic N, Rangan S, Roh W, Sayeed AM (2016) An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Top Signal Process 10(3):436–453CrossRefGoogle Scholar
  14. 14.
    Zhou Y, Herdin M, Sayeed AM, Bonek E (2006) Experimental study of MIMO channel statistics and capacity via virtual channel representationGoogle Scholar
  15. 15.
    Payami S, Tufvesson F (2012) Channel measurements and analysis for very large array systems at 2.6 GHz. In: 2012 6th European conference on antennas and propagation (EUCAP), pp 433–437Google Scholar
  16. 16.
    Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666.  https://doi.org/10.1109/TIT.2007.909108 MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Donoho DL, Elad M (2003) Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proc Natl Acad Sci 100(5):2197–2202MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244.  https://doi.org/10.1162/15324430152748236 MathSciNetzbMATHGoogle Scholar
  19. 19.
    Wipf DP, Rao BD (2004) Sparse Bayesian learning for basis selection. IEEE Trans Signal Process 52(8):2153–2164MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Arjoune Y, Kaabouch N, Ghazi HE, Tamtaoui A (2017) Compressive sensing: performance comparison of sparse recovery algorithms. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), pp 1–7Google Scholar
  21. 21.
    Lee J, Gil G, Lee YH (2016) Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications. IEEE Trans Commun 64(6):2370–2386CrossRefGoogle Scholar
  22. 22.
    Dai J, Liu A, Lau VKN (2018) FDD massive MIMO channel estimation with arbitrary 2D-array geometry. IEEE Trans Signal Process 66(10):2584–2599MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Rao X, Lau VKN (2014) Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems. IEEE Trans Signal Process 62(12):3261–3271MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Tseng CC, Wu JY, Lee TS (2016) Enhanced compressive downlink CSI recovery for FDD massive MIMO systems using weighted block \({\ell _1}\)-minimization. IEEE Trans Commun 64(3):1055–1067CrossRefGoogle Scholar
  25. 25.
    Sharma E, Budhiraja R, Vasudevan K, Hanzo L (2018) Full-duplex massive MIMO multi-pair two-way AF relaying: energy efficiency optimization. IEEE Trans Commun 66(8):3322–3340CrossRefGoogle Scholar
  26. 26.
    Ngo HQ, Suraweera HA, Matthaiou M, Larsson EG (2014) Multipair full-duplex relaying with massive arrays and linear processing. IEEE J Sel Areas Commun 32(9):1721–1737CrossRefGoogle Scholar
  27. 27.
    Tse D, Viswanath P (2005) Fundamentals of wireless communication. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  28. 28.
    Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, BerlinzbMATHGoogle Scholar
  29. 29.
    Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes, 4th edn. McGraw Hill, BostonGoogle Scholar
  30. 30.
    Petersen KB, Pedersen MS (2012) The matrix cookbook, version 20121115. http://www2.imm.dtu.dk/pubdb/p.php?3274
  31. 31.
    Biguesh M, Gershman AB (2006) Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals. IEEE Trans Signal Process 54(3):884–893CrossRefzbMATHGoogle Scholar
  32. 32.
    Björnson E, Hoydis J, Sanguinetti L (2017) Massive MIMO networks: spectral, energy, and hardware efficiency. Found Trends Signal Process 11(3–4):154–655CrossRefGoogle Scholar
  33. 33.
    Kay SM (1993) Fundamentals of statistical signal processing, volume I: estimation theory. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar

Copyright information

© CSI Publications 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIndian Institute of Technology KanpurKanpurIndia

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