Wideband Beamspace Massive MIMO Channel Estimation
DOI:
https://doi.org/10.54097/p90khg63Keywords:
Massive MIMO, MMWave communication, Channel estimation, Beamspace, VAMPAbstract
Millimeter-wave (mmWave) Massive multiple-input multiple-output (Massive MIMO) communication can provide high-speed network services for emerging application scenarios due to the abundant spectrum resources in the high-frequency band, which has emerged as a key technology for future wireless networks. Beamspace Massive MIMO systems equipped with lensed antenna arrays (LAA) have attracted considerable attention from industry and academic since it is an effective solution with low power and low cost. However, the beam squint effect causes beamspace channel estimation to be significantly complicated in wideband beamspace Massive MIMO systems. To address this problem, we investigate a channel estimator based on the vector approximate message passing (VAMP) algorithm to improve the estimation performance. Specifically, the wideband beamspace channel estimation is firstly considered as the two-dimensional (2D) image reconstruction problem. Subsequently, by the VAMP-based scheme, the 2D natural image is accurately sparse reconstructed from noisy linear measurements, which effectively solve the channel estimation problem. Simulation results verify the effectiveness of the proposed method and highlight its excellent performance in terms of the channel estimation.
Downloads
References
[1] M. Xiao, S. Mumtaz, Y. Huang, L. Dai, Y. Li, M. Matthaiou, G. K. Karagiannidis, E. Bj¨ornson, K. Yang, I.Chih-Lin, et al., “Millimeter wave communications for future mobile networks,” IEEE Journal on SelectedAreas in Communications, vol. 35, no. 9, pp. 1909–1935, (2017)
[2] A. L. Swindlehurst, E. Ayanoglu, P. Heydari, and F. Capolino, “Millimeter-wave Massive MIMO: The next wireless revolution ?,” IEEE Communications Magazine, vol. 52, no. 9, pp. 56–62, (2014)
[3] X.-H. You, C.-X. Wang, J. Huang, X. Gao, Z. Zhang, M. Wang, Y. Huang, C. Zhang, etal., “Towards 6g wireless communication networks: Vision, enabling technologies, and new paradigm shifts,” Science China Information Sciences, vol. 64, pp. 1–74, (2021)
[4] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5g cellular: It will work!,” IEEE Access, vol.1, pp. 335–349, (2013)
[5] L. Wei, R. Q. Hu, Y. Qian, and G. Wu, “Key elements to enable millimeter wave communications for 5g wireless systems,” IEEE Wireless Communications, vol. 21, no. 6, pp. 136–143, (2014)
[6] X. Gao, L. Dai, S. Han, I. Chih-Lin, and R. W. Heath, “Energy-efficient hybrid analog and digital precoding for mmwave mimo systems with large antenna arrays,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 4, pp. 998–1009,( 2016)
[7] J. Brady, N. Behdad, and A. M. Sayeed, “Beamspace mimo for millimeter-wave communications: System architecture, modeling, analysis, and measurements,” IEEE Transactions on Antennas and Propagation, vol. 61, no. 7, pp. 3814–3827, (2013)
[8] Y. Zeng, R. Zhang, and Z. N. Chen, “Electromagnetic lens-focusing an-tenna enabled Massive mimo: Performance improvement and cost reduc-tion,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1194–1206, (2014)
[9] O. Quevedo-Teruel, M. Ebrahimpouri, and F. Ghasemifard, “Lens anten-nas for 5g communications systems,” IEEE Communications Magazine, vol. 56, no. 7, pp. 36–41, (2018)
[10] Y. Zeng and R. Zhang, “Millimeter wave mimo with lens antenna array: A new path division multiplexing paradigm,” IEEE Transactions on Communications, vol. 64, no. 4, pp. 1557–1571, (2016)
[11] A. Sayeed and J. Brady, “Beamspace mimo for high-dimensional multiuser communication at millimeter-wave frequencies,” in 2013 IEEE global communications conference (GLOBECOM), pp. 3679–3684, IEEE, (2013)
[12] X. Gao, L. Dai, S. Han, I. Chih-Lin, and F. Adachi, “Beamspace channel estimation for 3D lens-based millimeter-wave Massive mimo systems,” in 2016 International Conference on Wireless Communications &Signal Processing (WCSP), pp. 1–5, IEEE, (2016)
[13] J. Yang, C.-K. Wen, S. Jin, and F. Gao, “Beamspace channel estimation in mmwave systems via cosparse image reconstruction technique,” IEEE Transactions on Communications, vol. 66, no. 10, pp. 4767–4782, (2018)
[14] 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 Transactions on Wireless Communications, vol. 16, no. 9, pp. 6010–6021, (2017)
[15] Z. Gao, C. Hu, L. Dai, and Z. Wang, “Channel estimation for millimeterwave Massive MIMO with hybrid precoding over frequency-selective fading channels,” IEEE Communications Letters, vol. 20, no. 6, pp.1259–1262, (2016)
[16] B. Wang, F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial-and frequency wideband effects in millimeter-wave Massive MIMO systems,” IEEE Transactions on Signal Processing, vol.66, no.13, pp. 3393–3406, (2018)
[17] X. Gao, L. Dai, S. Zhou, A. M. Sayeed, and L. Hanzo, “Wideband beamspace channel estimation for millimeter-wave mimo systems relying on lens antenna arrays,” IEEE Transactions on Signal Processing, vol. 67, no. 18, pp.4809–4824, (2019)
[18] X. Cheng, J. Deng, and S. Li, “Wideband Channel Estimation for Millimeter Wave Beamspace MIMO,” IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7221–7225, (2021)
[19] X. Mo, W. Ma, L. Gui, L. Zhang, and X. Sang, “Beamspace Channel Estimation With Beam Squint Effectfor the Millimeter-Wave MIMO-OFDM Systems,” IEEE Access, vol. 9, pp. 153037–153049, (2021)
[20] D. L. Donoho, A. Maleki and A. Montanari, “Message passing algorithms for compressed sensing: I. Motivation and construction,” in 2010 IEEE Inf. Theory Workshop (ITW Cairo), pp. 1–5, IEEE, (2010)
[21] He, H., Wen, C.K., Jin, S., Li, G.Y, “Deep learning-based channel estimation for beamspace mmwave Massive mimo systems,” IEEE Wireless Communications Letters vol.7, no.5, pp.852–855, (2018)
[22] H. He, R. Wang, W. Jin, S. Jin, C. -K. Wen and G. Y. Li, “Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach,” IEEE Transactions on Wireless Communications, vol. 22, no. 3, pp. 1808-1822, (2023)
[23] R. W. Heath, N. Gonzalez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, ”An overview of signal processing techniques for millimeter wave mimo systems,” IEEE journal of selected topics in signal processing, vol. 10, no. 3, pp. 436–453, (2016)
[24] X. Gao, L. Dai, S. Zhou, A. M. Sayeed, and L. Hanzo, ”Beamspace channel estimation for wideband millimeter-wave mimo with lens antenna array,” in 2018 IEEE International Conference on Communications (ICC), pp. 1–6, IEEE, (2018)
[25] B. Wang, F. Gao, G. Y. Li, S. Jin, and H. Lin, “Wideband channel estimation for mmwave Massive mimo systems with beam squint effect,” in 2018 IEEE Global Communications Conference (GLOBECOM), pp.1–6, IEEE, (2018)
[26] Y. Nie, “Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation,” IEEE Access, vol. 8, pp. 124783–124791, (2020)
[27] H. He, C. -K. Wen, S. Jin and G. Y. Li, “Model-Driven Deep Learning for MIMO Detection,” IEEE Transactions on Signal Processing, vol. 68, pp. 1702–1715, (2020)
[28] X. Wei, C. Hu and L. Dai, “Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems,” IEEE Transactions on Communications, vol. 69, no. 1, pp. 182–193, (2021)
[29] S. Rangan, P. Schniter and A. K. Fletcher, “ Vector Approximate Message Passing,” IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6664–6684, (2019)
[30] M. Borgerding, P. Schniter and S. Rangan, “AMP-Inspired Deep Networks for Sparse Linear Inverse Prob-lems,” IEEE Transactions on Signal Processing, vol. 65, no. 16, pp. 4293–4308, (2017)
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Computing and Electronic Information Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
