Two New Algorithm for Millimeter-Wave Massive MIMO Channel Estimation Based on Lens Antenna Array

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

The use of multi-input multi-output (MIMO) systems in addition to increasing capacity, reducing the destructive effects of multi-path phenomena, reducing interference with other users, will lead to higher data rates. On the other hand, the use of millimeter-wave technology and work in high-frequency bands can prevent issues such as traffic and interference, and can significantly increase the data rates, spectral efficiency and the bandwidth. The millimeter-wave massive MIMO with the lens antenna array can significantly reduce the number of radio-frequency chains. In this paper, two novel algorithms are proposed for channel estimation in millimeter-wave massive MIMO. In this regard, a new algorithm using the compressive sensing based on the convex optimization is presented for channel estimation with high accuracy and low complexity. Then, the second new algorithm based on the greedy methods is provided. One of the benefits of this algorithm is its reduced computational complexity, and its high recovery speed. Finally, both proposed algorithms are compared with other existing algorithms. The simulation results confirm that the proposed algorithms outperform the existing algorithms.

Keywords

Main Subjects


[1] S. Han, C.-L. I, Z. Xu, and C. Rowell, “Large-scale antenna systems with hybrid precoding analog and digital beamforming for millimeter wave 5G,” IEEE Commun. Mag., vol. 53, no. 1, pp. 186–194, Jan. 2015.
[2] L. Wei, R. Q. Hu, Y. Qian, and G. Wu, “Key elements to enable millimeter wave communications for 5G wireless systems,” IEEE Wireless Commun., vol. 21, no. 6, pp. 136–143, Dec. 2014.
[3] S. Kutty and D. Sen, “Beamforming for Millimeter Wave Communications: An Inclusive Survey,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 949–973, 2016.
[4] 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 Process. Mag., vol. 30, no. 1, pp. 40–60, Jan. 2013.
[5] S. Mumtaz, J. Rodriguez, and L. Dai, mmWave Massive MIMO: A Paradigm for 5G, Academic Press, 1st Edition. 2016.
[6] R. Cao, T. F. Wong, H. Gao, D. Wang, and Y. Lu, “Blind Channel Direction Separation Against Pilot Spoofing Attack in Massive MIMO System,” 26th Eur. Signal Process. Conf., pp. 2577–2581, 2018.
[7] P. Pasangi, M. Atashbar, and M. Mohassel Feghhi, “Blind downlink channel estimation for TDD‐based multiuser massive MIMO in the presence of nonlinear HPA,” ETRI Journal, vol. 41, pp. 426–436, Mar. 2019.
[8] Y. Zeng and R. Zhang, “Millimeter wave MIMO with lens antenna array: A new path division multiplexing paradigm,” IEEE Trans. Commun., vol. 64, no. 4, pp. 1557–1571, Apr. 2016.
[9] Y. Zeng, R. Zhang, and Z. N. Chen, “Electromagnetic lens-focusing antenna enabled massive MIMO: Performance improvement and cost reduction,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1194–1206, Jun. 2014.
[10] X. Gao, L. Dai, Z. Chen, Z. Wang, and Z. Zhang, “Near-optimal beam selection for beamspace mmWave massive MIMO systems,” IEEE Commun. Lett., vol. 20, no. 5, pp. 1054–1057, May 2016.
[11] A. Alkhateeb, G. Leus, and R. W. Heath, “Compressed sensing based multi-user millimeter wave systems: How many measurements are needed?” in Proc. IEEE ICASSP, Apr. 2015, pp. 2909–2913.
[12] W. U. Bajwa, J. Haupt, A. Sayeed, and R. Nowak, “Compressed channel sensing: A new approach to estimating sparse multipath channels,” Proc. IEEE, vol. 98, no. 6, pp. 1058–1076, Jun. 2010.
[13] X. Gao, L. Dai, S. Han, I. Chih-Lin, and X. Wang, “Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array,” IEEE Trans. Wirel. Commun., vol. 16, no. 9, pp. 6010–6021, 2017.
[14] B. Wang, L. Dai, Z. Wang, N. Ge and S. Zhou, "Spectrum and Energy-Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array," IEEE Journal on Selected Areas in Communications, vol. 35, no. 10, pp. 2370-2382, Oct. 2017.
[15] A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding for millimeter wave cellular systems,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 831–846, Oct. 2014.
[16] J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007.
[17] J. A. T. Needell, Deanna, “CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,” Appl. Comput. Harmon. Anal., vol. 26, no.3, pp. 301–321, 2009.
[18] J. Choi, V. Va, N. González-Prelcic, R. Daniels, C. R. Bhat, and R. W. Heath, “Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing,” IEEE Commun. Mag., vol. 54, no. 12, pp. 160–167, 2016.
[19] L. Yang, Y. Zeng, and R. Zhang, “Efficient channel estimation for millimeter wave MIMO with limited RF chains,” in Proc. IEEE ICC, May 2016, pp. 1–6.
[20] Y. Zeng, S. Member, R. Zhang, and Z. N. Chen, “Electromagnetic Lens-Focusing Antenna Enabled Massive MIMO : Performance Improvement and Cost Reduction,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1194–1206, 2014.
[21] J. A. Tropp, A. C. Gilbert, and M. J. Strauss, “Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit,” Signal Processing, vol. 86, no. 3, pp. 572–588, 2006.
[22] M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx, Sep. 2013.
[23] N. B. Karahanoglu and H. Erdogan, Compressed Sensing Signal Recovery via Forward-Backward Pursuit, 2012.
[24] T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, “Sparsity adaptive matching pursuit algorithm for practical compressed sensing”, pp. 581–587, 2008.
[25] Y. Wang, J. Chen, and W. Fang, “TST-MUSIC for Joint DOA-Delay Estimation,” vol. 49, no. 4, pp. 721–729, 2001.