Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

beam split; channel estimation; federated learning; near-field; sparse Bayesian learning; Terahertz; Bayesian learning; Beam split; Federated learning; Learning techniques; Near fields; Sparse bayesian; Sparse bayesian learning; Tera Hertz; Terahertz band; Via arrays; Computer Networks and Communications; Millimeter wave communication; Estimation; Array signal processing; Hardware; Bandwidth; Perturbation methods; eess.SP; Computer Science - Information Theory; Mathematics - Information Theory

Abstract :

[en] For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cramér-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.

Disciplines :

Computer science

Author, co-author :

Elbir, Ahmet M. ; University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust, Esch-sur-Alzette, Luxembourg

Shi, Wei ; Carleton University, School of Information Technology, Ottawa, Canada

Papazafeiropoulos, Anastasios K. ; University of Hertfordshire, Communications and Intelligent Systems Research Group, Hatfield, United Kingdom

Kourtessis, Pandelis ; University of Hertfordshire, Communications and Intelligent Systems Research Group, Hatfield, United Kingdom

CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

External co-authors :

yes

Language :

English

Title :

Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

Publication date :

2023

Journal title :

IEEE Open Journal of the Communications Society

eISSN :

2644-125X

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

892 - 907

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx7/8782661/10008219/10089857.pdf?arnumber=10089857

Funders :

Horizon Project TERRAMETA
Natural Sciences and Engineering Research Council of Canada
Ericsson Canada

Funding text :

This work was supported in part by the Horizon Project TERRAMETA; in part by the Natural Sciences and Engineering Research Council of Canada (NSERC); and in part by Ericsson Canada.

Data Set :

https://doi.org/10.1109/OJCOMS.2023.3263625

Commentary :

Accepted Paper in IEEE Open Journal of Communications Society

Available on ORBilu :

since 30 November 2023

Statistics

Number of views

60 (1 by Unilu)

Number of downloads

88 (2 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

WoS citations^™

Bibliography

H. Sarieddeen, M.-S. Alouini, and T. Y. Al-Naffouri, "An overview of signal processing techniques for terahertz communications, " Proc. IEEE, vol. 109, no. 10, pp. 1628-1665, Oct. 2021.
A. M. Elbir, K. V. Mishra, and S. Chatzinotas, "Terahertz-band joint ultra-massive MIMO radar-communications: Model-based and modelfree hybrid beamforming, " IEEE J. Sel. Topics Signal Process., vol. 15, no. 6, pp. 1468-1483, Nov. 2021.
"Spectrum horizons, RM-11795, FCC 19-19." Mar. 2019. Accessed: Nov. 22, 2022. [Online]. Available: https://www.federalregister.gov/documents/2019/06/04/2019-10925/spectrum-horizons
I. F. Akyildiz, C. Han, Z. Hu, S. Nie, and J. M. Jornet, "Terahertz band communication: An old problem revisited and research directions for the next decade, " IEEE Trans. Commun., vol. 70, no. 6, pp. 4250-4285, Jun. 2022.
A. M. Elbir, K. V. Mishra, S. Chatzinotas, and M. Bennis, "Terahertz-band integrated sensing and communications: Challenges and opportunities, " Aug. 2022, arXiv:2208.01235.
H. Yuan, N. Yang, K. Yang, C. Han, and J. An, "Hybrid beamforming for terahertz multi-carrier systems over frequency selective fading, " IEEE Trans. Commun., vol. 68, no. 10, pp. 6186-6199, Oct. 2020.
J. Tan and L. Dai, "Wideband beam tracking in THz massive MIMO systems, " IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1693-1710, Jun. 2021.
S. Tarboush et al., "TeraMIMO: A channel simulator for wideband ultra-massive MIMO terahertz communications, " Apr. 2021, arXiv:2104.11054.
H. Sarieddeen, M.-S. Alouini, and T. Y. Al-Naffouri, "Terahertzband ultra-massive spatial modulation MIMO, " IEEE J. Sel. Areas Commun., vol. 37, no. 9, pp. 2040-2052, Sep. 2019.
A. Faisal, H. Sarieddeen, H. Dahrouj, T. Y. Al-Naffouri, and M.-S. Alouini, "Ultramassive MIMO systems at terahertz bands: Prospects and challenges, " IEEE Veh. Technol. Mag., vol. 15, no. 4, pp. 33-42, Dec. 2020.
B. Ning, Z. Chen, W. Chen, Y. Du, and J. Fang, "Terahertz multiuser massive MIMO with intelligent reflecting surface: Beam training and hybrid beamforming, " IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1376-1393, Feb. 2021.
B. Wang, M. Jian, F. Gao, G. Y. Li, and H. Lin, "Beam squint and channel estimation for wideband mmWave massive MIMO-OFDM systems, " IEEE Trans. Signal Process., vol. 67, no. 23, pp. 5893-5908, Dec. 2019.
C. Lin and G. Y. L. Li, "Terahertz communications: An arrayof-subarrays solution, " IEEE Commun. Mag., vol. 54, no. 12, pp. 124-131, Dec. 2016.
O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, "Spatially sparse precoding in millimeter wave MIMO systems, " IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1499-1513, Mar. 2014.
R. W. Heath, N. González-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, "An overview of signal processing techniques for millimeter wave MIMO systems, " IEEE J. Sel. Topics Signal Process., vol. 10, no. 3, pp. 436-453, Apr. 2016.
R. Méndez-Rial, C. Rusu, A. Alkhateeb, N. González-Prelcic, and R. W. Heath, "Channel estimation and hybrid combining for mmWave: Phase shifters or switches?" in Proc. IEEE Inf. Theory Appl. Workshop, 2015, pp. 90-97.
A. Alkhateeb, G. Leus, and R. W. Heath, "Limited feedback hybrid precoding for multi-user millimeter wave systems, " IEEE Trans. Wireless Commun., vol. 14, no. 11, pp. 6481-6494, Nov. 2015.
A. M. Elbir, K. V. Mishra, S. A. Vorobyov, and R. W. Heath, "Twentyfive years of advances in beamforming: From convex and nonconvex optimization to learning techniques, " Nov. 2022, arXiv:2211.02165.
J. Tan and L. Dai, "Wideband channel estimation for THz massive MIMO, " China Commun., vol. 18, no. 5, pp. 66-80, May 2021.
K. Wu, W. Ni, T. Su, R. P. Liu, and Y. J. Guo, "Exploiting spatialwideband effect for fast AoA estimation at lens antenna array, " IEEE J. Sel. Topics Signal Process., vol. 13, no. 5, pp. 902-917, Sep. 2019.
M. Wang, F. Gao, N. Shlezinger, M. F. Flanagan, and Y. C. Eldar, "A block sparsity based estimator for mmWave massive MIMO channels with beam squint, " IEEE Trans. Signal Process., vol. 68, pp. 49-64, Nov. 2019, doi: 10.1109/TSP.2019.2956677.
A. Brighente, M. Cerutti, M. Nicoli, S. Tomasin, and U. Spagnolini, "Estimation of wideband dynamic mmWave and THz channels for 5G systems and beyond, " IEEE J. Sel. Areas Commun., vol. 38, no. 9, pp. 2026-2040, Sep. 2020.
S. Srivastava, A. Tripathi, N. Varshney, A. K. Jagannatham, and L. Hanzo, "Hybrid transceiver design for tera-hertz MIMO systems relying on Bayesian learning aided sparse channel estimation, " Sep. 2021, arXiv:2109.09664.
W. Yu, Y. Shen, H. He, X. Yu, J. Zhang, and K. B. Letaief, "Hybrid Far-and near-field channel estimation for THz ultra-massive MIMO via fixed point networks, " May 2022, arXiv:2205.04944.
E. Balevi and J. G. Andrews, "Wideband channel estimation with a generative adversarial network, " IEEE Trans. Wireless Commun., vol. 20, no. 5, pp. 3049-3060, May 2021.
B. Peng, S. Wesemann, K. Guan, W. Templ, and T. Kürner, "Precoding and detection for broadband single carrier terahertz massive MIMO systems using LSQR algorithm, " IEEE Trans. Wireless Commun., vol. 18, no. 2, pp. 1026-1040, Feb. 2019.
S. Nie and I. F. Akyildiz, "Deep kernel learning-based channel estimation in ultra-massive MIMO communications at 0.06-10 THz, " in Proc. IEEE Globecom Workshops (GC Wkshps), Dec. 2019, pp. 1-6.
Y. Chen and C. Han, "Deep CNN-based spherical-wave channel estimation for terahertz ultra-massive MIMO systems, " in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2020, pp. 1-6.
K. Dovelos, M. Matthaiou, H. Q. Ngo, and B. Bellalta, "Channel estimation and hybrid combining for wideband terahertz massive MIMO systems, " IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1604-1620, Jun. 2021.
A. M. Elbir, W. Shi, K. V. Mishra, and S. Chatzinotas, "Federated multi-task learning for THz wideband channel and DoA estimation, " Jul. 2022, arXiv:2207.06017.
Z. Sha and Z. Wang, "Channel estimation and equalization for terahertz receiver with RF impairments, " IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1621-1635, Jun. 2021.
J. Gao, C. Zhong, G. Y. Li, J. B. Soriaga, and A. Behboodi, "Deep learning-based channel estimation for wideband hybrid mmWave massive MIMO, " May 2022, arXiv:2205.05202.
B. Wang, F. Gao, S. Jin, H. Lin, and G. Y. Li, "Spatial-and frequencywideband effects in millimeter-wave massive MIMO systems, " IEEE Trans. Signal Process., vol. 66, no. 13, pp. 3393-3406, Jul. 2018.
M. Cui, L. Dai, R. Schober, and L. Hanzo, "Near-field wideband beamforming for extremely large antenna arrays, " Sep. 2021, arXiv:2109.10054.
J.-J. Jiang, F.-J. Duan, J. Chen, Y.-C. Li, and X.-N. Hua, "Mixed nearfield and far-field sources localization using the uniform linear sensor array, " IEEE Sensors J., vol. 13, no. 8, pp. 3136-3143, Aug. 2013.
I.-S. Kim and J. Choi, "Spatial wideband channel estimation for mmWave massive MIMO systems with hybrid architectures and lowresolution ADCs, " IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 4016-4029, Jun. 2021.
A. M. Elbir and T. E. Tuncer, "2-D DOA and mutual coupling coefficient estimation for arbitrary array structures with single and multiple snapshots, " Digit. Signal Process., vol. 54, pp. 75-86, Jul. 2016.
Z.-M. Liu, Z.-T. Huang, and Y.-Y. Zhou, "An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning, " IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 1-11, Oct. 2012.
H. Tang, J. Wang, and L. He, "Off-grid sparse Bayesian learningbased channel estimation for mmWave massive MIMO uplink, " IEEE Wireless Commun. Lett., vol. 8, no. 1, pp. 45-48, Feb. 2019.
M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine, " J. Mach. Learn. Res., vol. 1, pp. 211-244, Sep. 2001.
A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm, " J. Roy. Stat. Soc. Ser. B, Methodol., vol. 39, no. 1, pp. 1-22, Sep. 1977.
B. M. Radich and K. M. Buckley, "Single-snapshot DOA estimation and source number detection, " IEEE Signal Process. Lett., vol. 4, no. 4, pp. 109-111, Apr. 1997.
R. Schmidt, "Multiple emitter location and signal parameter estimation, " IEEE Trans. Antennas Propag., vol. AP-34, no. 3, pp. 276-280, Mar. 1986.
E. J. Candes and M. B. Wakin, "An introduction to compressive sampling, " IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
X. Tan and J. Li, "Compressed sensing via sparse Bayesian learning and Gibbs sampling, " in Proc. IEEE 13th Digit. Signal Process. Workshop 5th IEEE Signal Process. Educ. Workshop, Jan. 2009, pp. 690-695.
Z.-M. Liu and Y.-Y. Zhou, "A unified framework and sparse Bayesian perspective for direction-of-arrival estimation in the presence of array imperfections, " IEEE Trans. Signal Process., vol. 61, no. 15, pp. 3786-3798, Aug. 2013.
K. M. S. Huq, S. A. Busari, J. Rodriguez, V. Frascolla, W. Bazzi, and D. C. Sicker, "Terahertz-enabled wireless system for beyond-5G ultrafast networks: A brief survey, " IEEE Netw., vol. 33, no. 4, pp. 89-95, Jul./Aug. 2019.
Y. Lu, M. Hao, and R. Mackenzie, "Reconfigurable intelligent surface based hybrid precoding for THz communications, " Intell. Converg. Netw., vol. 3, no. 1, pp. 103-118, 2022.
A. Alkhateeb and R. W. Heath, "Frequency selective hybrid precoding for limited feedback millimeter wave systems, " IEEE Trans. Commun., vol. 64, no. 5, pp. 1801-1818, May 2016.
A. M. Elbir and S. Chatzinotas, "BSA-OMP: Beam-splitaware orthogonal matching pursuit for THz channel estimation, " IEEE Wireless Commun. Lett., early access, Feb. 7, 2023, doi: 10.1109/LWC.2023.3242699.
B. Wang et al., "Spatial-wideband effect in massive MIMO with application in mmWave systems, " IEEE Commun. Mag., vol. 56, no. 12, pp. 134-141, Dec. 2018.
D. Fan et al., "Angle domain channel estimation in hybrid millimeter wave massive MIMO systems, " IEEE Trans. Wireless Commun., vol. 17, no. 12, pp. 8165-8179, Dec. 2018.
X. Wu, W.-P. Zhu, and J. Yan, "Direction of arrival estimation for off-grid signals based on sparse Bayesian learning, " IEEE Sensors J., vol. 16, no. 7, pp. 2004-2016, Apr. 2016.
A. M. Elbir and T. E. Tuncer, "Far-field DOA estimation and nearfield localization for multipath signals, " Radio Sci., vol. 49, no. 9, pp. 765-776, Sep. 2014.
A. M. Elbir, K. V. Mishra, and S. Chatzinotas, "NBA-OMP: Nearfield beam-split-aware orthogonal matching pursuit for wideband THz channel estimation, " Feb. 2023, arXiv:2302.01682.
A. M. Elbir, A. K. Papazafeiropoulos, and S. Chatzinotas, "Federated learning for physical layer design, " IEEE Commun. Mag., vol. 59, no. 11, pp. 81-87, Nov. 2021.
A. M. Elbir and S. Coleri, "Federated learning for channel estimation in conventional and RIS-assisted massive MIMO, " IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 4255-4268, Jun. 2022.
A. M. Elbir, S. Coleri, A. K. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, "A hybrid architecture for federated and centralized learning, " IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 3, pp. 1529-1542, Sep. 2022.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-efficient learning of deep networks from decentralized data, " Feb. 2016, arXiv:1602.05629.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated learning: Challenges, methods, and future directions, " IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50-60, May 2020.
S. Srivastava, A. Mishra, A. Rajoriya, A. K. Jagannatham, and G. Ascheid, "Quasi-static and time-selective channel estimation for block-sparse millimeter wave hybrid MIMO systems: Sparse Bayesian learning (SBL) based approaches, " IEEE Trans. Signal Process., vol. 67, no. 5, pp. 1251-1266, Mar. 2019.
J. Dai and H. C. So, "Sparse Bayesian learning approach for outlierresistant direction-of-arrival estimation, " IEEE Trans. Signal Process., vol. 66, no. 3, pp. 744-756, Feb. 2018.
P. Stoica and A. Nehorai, "MUSIC, maximum likelihood, and Cramér-Rao bound: Further results and comparisons, " IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 12, pp. 2140-2150, Dec. 1990.