Sparse Bayesian Learning (SBL) Based Channel Estimation for Millimeter-Wave Hybrid Massive MIMO System
Keywords:
Millimeter-wave (mmWave), Sparse Bayesian Learning (SBL), Channel estimation, Massive MIMO, Sparse ChannelAbstract
Reduced system complexity yields advantages that extend to enforcement obligations as well. The task of acquiring precise channel information for hybrid precoding in millimeter-wave (mmWave) systems is challenging for a multitude of reasons. Among the methods employed are analog precoding, a large number of antennas, and a pre-beamforming state with a low signal-to-noise ratio. To address this issue, an innovative channel estimation method is necessary. A massive MIMO channel estimation technique is suggested by the authors for hybrid millimeter-wave wireless networks. This scheme utilizes SBL and capitalizes on the spatial sparsity of wireless channels resulting from focused propagation. Spherical sparsity and response matrices for quantized directional cosines at the transmitting and receiving antenna arrays are distinctive characteristics of the enormous MIMO channel. A Sparse Bayesian Learning (SBL) channel estimation method utilizing Expectation Maximization (EM) is engineered. Using the NYUSIM millimeter channel simulator, the actual mmWave channel model is estimated so that the submitted techniques can be validated. In comparison to least-squares and orthogonal matching pursuit (OMP) techniques, SBL-based approaches for channel estimation demonstrate superior performance, as demonstrated by the simulation outcomes.
Downloads
References
A. M. Hamed, R. K. Rao, Energy and spectral efficiencies of M-CPFSK in fading and shadowing wireless channels, Phys. Commun. 30 (2018) 204-212.
J.-B. Doré, R. Gerzaguet, N. Cassiau, D. Ktenas, Waveform contenders for 5G: Description, analysis and comparison, Phys. Commun. 24 (2017) 46-61[.
J. Hoydis, S. Brink, M. Debbah, Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need?, IEEE J. Sel. Areas Commun. 31(2) (2013) 160-171.
E. Larsson, O. Edfors, F. Tufvesson, T. Marzetta, Massive MIMO for next generation wireless systems, IEEE Commun. Mag. 52(2) (2014) 186-195.
S. Rajoria, A. Trivedi, W. W. Godfrey, A comprehensive survey: Small cell meets massive MIMO, Phys. Commun. 26 (2018) 40-49.
N. Al-Falahy, O. Y. K. Alani, Millimetre wave frequency band as a candidate spectrum for 5G network architecture: A survey, Phys. Commun. 32 (2019) 120-144.
W. Roh, J. –Y. Seol, J. Park, B. Lee, J. Lee, Y. Kim, J. Cho, K. Cheun, Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results, IEEE Commun. Mag. 52(2) (2014) 106-113.
Arjoune, Youness, and Saleh Faruque. "Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications." Physical Communication 51 (2022): 101534.
Özbek, Berna, et al. "Hybrid beamforming strategies for secure multicell multiuser mmWave MIMO communications." Physical Communication 46 (2021): 101319
A. Swindlehurst, E. Ayanoglu, P. Heydari, F. Capolino, Millimeter-wave massive MIMO: The next wireless revolution?, IEEE Commun. Mag. 52(9) (2014) 56-62.
S. Wang, L. Li, R. Ruby, P. Li, A general hybrid precoding scheme for millimeter-wave massive MIMO systems, Wirel. Netw. 26(2) (2019) 1331-1345.
Liao, Yong, Zisong Yin, and Guodong Sun. "Joint fast time-varying channel estimation with noise elimination and ICI cancellation for LTE-V2X systems." Physical Communication 55 (2022): 101900.
Molazadeh, Amirhossein, and Mehrdad Ardebilipour. "Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems." Physical Communication 55 (2022): 101933.
Y. Wang, W. Zou, Hybrid precoding design for millimeter-wave systems with the partially‐connected structure, IET Commun. 14(4) (2020) 561-567.
A. Uwaechia, N. Mahyuddin, A comprehensive survey on millimeter-wave communications for Fifth-generation wireless networks: Feasibility and challenges, IEEE Access 8 (2020) 62367-62414.
R. Heath, N. Gonzalez-Prelcic, S. Rangan, W. Roh, A. Sayeed, An overview of signal processing techniques for millimeter-wave MIMO systems, IEEE J. Sel. Top. Signal Process. 10(3) (2016) 436-453.
A. Alkhateeb, O. El Ayach, G. Leus, R. Heath, Channel estimation and hybrid precoding for millimeter-wave cellular systems, IEEE J. Sel. Top. Signal Process. 8(5) (2014) 831-846.
M. Rossi, A. Haimovich, Y. Eldar, Spatial compressive sensing for MIMO radar, IEEE Trans. Signal Process. 62(2) (2014) 419-430.
J. Lee, G. Gil, Y. Lee, Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter-wave communications, IEEE Trans. Commun. 64(6) (2016) 2370-2386.
Y. Huang, L. Wan, S. Zhou, Z. Wang, J. Huang, (2014). Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning. Physical Communication, 13, 156-167.
I. F. Gorodnitsky, B. D. Rao, Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm, IEEE Trans. Signal Process. 45(3) (1997) 600-616.
S. Sun, T. Rappaport, R. Heath, A. Nix, S. Rangan, Mimo for millimeter-wave wireless communications: Beamforming, spatial multiplexing, or both?, IEEE Commun. Mag. 52(12) (2014) 110-121.
K. Venugopal, A. Alkhateeb, N. G. Prelcic, and R. W. Heath, “Channel estimation for hybrid architecture-based wideband millimeter-wave systems,” IEEE J. Sel. Areas Commun. 35(9) (2017) 1996–2009.
R. T. Suryaprakash, M. Pajovic, K. J. Kim, P. Orlik, Millimeter-wave communications channel estimation via Bayesian group sparse recovery, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 3406–3410.
D. P. Wipf, B. D. Rao, Sparse Bayesian learning for basis selection, IEEE Trans. Signal Process. 52(8) (2004) 2153–2164.
J. Yang, C.-K. Wen, S. Jin, F. Gao, Beamspace channel estimation in MMWAVE systems via COSPARSE Image Reconstruction Technique, IEEE Trans. Commun. pp. 1–1, 2018.
Y. Wang, Z. Tian, S. Feng, P. Zhang, Efficient Channel Statistics Estimation for millimeter-wave MIMO Systems,” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
L. Dai, X. Gao, “Priori-aided channel tracking for millimeter-wave beamspace massive MIMO Systems,” 2016 URSI Asia-Pacific Radio Science Conference (URSI AP-RASC), 2016.
X. Lin, S. Wu, C. Jiang, L. Kuang, J. Yan, L. Hanzo, Estimation of broadband multiuser millimeter-wave massive MIMO-OFDM channels by exploiting their sparse structure, IEEE Trans. Wirel. Commun. 17(6) (2018) 3959–3973.
J. R. Fernández, N. G. Prelcic, K. Venugopal, R. W. Heath, Frequency-domain compressive channel estimation for frequencyselective hybrid millimeter-wave MIMO systems, IEEE Trans. Wirel. Commun. 17(5) (2018) 2946–2960.
W. Huang, Y. Huang, R. Zhao, S. He, L. Yang, Wideband millimeter-wave communication: Single carrier based hybrid precoding with sparse optimization, IEEE Trans. Veh. Technol. 67(10) (2018) 9696-9710.
S. Buzzi, C. D Andrea, T. Foggi, A. Ugolini, G. Colavolpe, Singlecarrier modulation versus OFDM for millimeter-wave wireless MIMO, IEEE Trans. Commun. 66(3) (2018) 1335–1348.
J. Mo, P. Schniter, and R. W. Heath, Channel estimation in broadband millimeter-wave mimo systems with few-bit adcs, IEEE Trans. Signal Process. 66(5) (2018) 1141–1154.
R. Prasad, C. R. Murthy, B. D. Rao, Joint approximately sparse channel estimation and data detection in OFDM systems using sparse Bayesian learning, IEEE Trans. Signal Process. 62(14) (2014) 3591–3603.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
