PDF (1.5 MB)
Collect
Submit Manuscript
Open Access

CGLS Method for Efficient Equalization of OFDM Systems Under Doubly Dispersive Fading Channels with an Application into 6G Communications in Smart Overcrowded

College of Engineering, Electrical Department, Zarqa University, Zarqa 13132, Jordan
Show Author Information

Abstract

Because of its superior multipath channel performance and spectrum efficiency, orthogonal frequency-division multiplexing (OFDM) is one of the most suitable candidates for the 6G physical layer in contemporary applications like smart cities and crowded environments in a social Internet of Things (S-IoT) ecosystem. The terahertz (THz) band, 1Tbit/s data rate, low latency, spectrum efficacy, and mobility of 1000 km/h are just a few of the requirements that 6G must meet. Using single-tap equalization, OFDM may reduce the multipath channel impact optimally. However, when the channel has doubly dispersive fading, inter-carrier interference starts to create errors. The worst doubly dispersive fading channel will be produced when THz and high-speed mobility are combined with OFDM as the physical layer for 6G. OFDM requires a complicated equalizer when using channels with doubly dispersive fading. On the basis of band factorization, time domain least squares QR (LSQR) iterative computing, and banded minimum mean squared error (BMMSE), a number of low-complexity equalizers for OFDM have been proposed. Conjugate gradient least squares (CGLS), a revolutionary iterative computation algorithm, is proposed in this study; the proposed equalization technique obtains the trade-off between computations and performance. Simulation results show that the proposed equalizer outperforms the existing BMMSE and LSQR algorithms over doubly dispersive fading channels.

References

[1]

M. H. Alsharif, M. S. Hossain, A. Jahid, M. A. Khan, B. J. Choi, and S. M. Mostafa, Milestones of wireless communication networks and technology prospect of next generation (6G), Comput. Mater. Continua, vol. 71, no. 3, pp. 4803–4818, 2022.

[2]

H. Attar, H. Issa, J. Ababneh, M. Abbasi, A. A. A. Solyman, M. Khosravi, and R. S. Agieb, 5G system overview for ongoing smart applications: Structure, requirements, and specifications, Comput. Intell. Neurosci., vol. 2022, p. 2476841, 2022.

[3]

A. A. A. Solyman and K. Yahya, Key performance requirement of future next wireless networks (6G), Bull. Electr. Eng. Inform., vol. 10, no. 6, pp. 3249–3255, 2021.

[4]

X. Sun, J. Qian, Z. Wang, J. Miao, and Y. Chai, Future of networked information society: A deeply interconnected “primitive society”, International Journal of Crowd Science, vol. 6, no. 4, pp. 178–183, 2022.

[5]

X. Xue, G. Li, D. Zhou, Y. Zhang, L. Zhang, Y. Zhao, Z. Feng, L. Cui, Z. Zhou, X. Sun, et al., Research roadmap of service ecosystems: A crowd intelligence perspective, International Journal of Crowd Science, vol. 6, no. 4, pp. 195–222, 2022.

[6]

M. H. Alsharif, M. A. M. Albreem, A. A. A. Solyman, and S. Kim, Toward 6G communication networks: Terahertz frequency challenges and open research issues, Comput. Mater. Continua, vol. 66, no. 3, pp. 2831–2842, 2021.

[7]

P. Jain, A. Gupta, N. Kumar, and M. Guizani, Dynamic and efficient spectrum utilization for 6G with THz, mmWave, and RF band, IEEE Trans. Veh. Technol., vol. 72, no. 3, pp. 3264–3273, 2023.

[8]

S. A. H. Mohsan, M. A. Khan, M. H. Alsharif, P. Uthansakul, and A. A. A. Solyman, Intelligent reflecting surfaces assisted UAV communications for massive networks: Current trends, challenges, and research directions, Sensors, vol. 22, no. 14, p. 5278, 2022.

[9]
A. A. Solyman, H. Attar, M. R. Khosravi, and B. Koyuncu, MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks, Int. J. Distrib. Sens. Netw., doi: 10.1177/1550147720912950.
[10]

H. H. Attar, A. A. A. Solyman, A. E. F. Mohamed, M. R. Khosravi, V. G. Menon, A. K. Bashir, and P. Tavallali, Efficient equalisers for OFDM and DFrFT-OCDM multicarrier systems in mobile E-health video broadcasting with machine learning perspectives, Phys. Commun., vol. 42, p. 101173, 2020.

[11]
C. Jeon, K. Li, J. R. Cavallaro, and C. Studer, On the achievable rates of decentralized equalization in massive MU-MIMO systems, in Proc. 2017 IEEE Int. Symp. Information Theory (ISIT), Aachen, Germany, 2017, pp. 1102–1106.
[12]

Y. S. Choi, P. J. Voltz, and F. A. Cassara, On channel estimation and detection for multicarrier signals in fast and selective Rayleigh fading channels, IEEE Trans. Commun., vol. 49, no. 8, pp. 1375–1387, 2001.

[13]

X. Cai and G. B. Giannakis, Bounding performance and suppressing intercarrier interference in wireless mobile OFDM, IEEE Trans. Commun., vol. 51, no. 12, pp. 2047–2056, 2003.

[14]

L. Rugini, P. Banelli, and G. Leus, Low-complexity banded equalizers for OFDM systems in Doppler spread channels, EURASIP J. Adv. Signal Process., vol. 2006, no. 1, p. 067404, 2006.

[15]

T. Hrycak, S. Das, G. Matz, and H. G. Feichtinger, Low complexity equalization for doubly selective channels modeled by a basis expansion, IEEE Trans. Signal Process., vol. 58, no. 11, pp. 5706–5719, 2010.

[16]

C. C. Paige and M. A. Saunders, LSQR: An algorithm for sparse linear equations and sparse least squares, ACM Trans. Math. Softw., vol. 8, no. 1, pp. 43–71, 1982.

[17]
G. Taubock, M. Hampejs, G. Matz, F. Hlawatsch, and K. Grochenig, LSQR-based ICI equalization for multicarrier communications in strongly dispersive and highly mobile environments, in Proc. 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications, Helsinki, Finland, 2007, pp. 1–5.
[18]
M. Saunders, CGLS: CG method for Ax = b and least squares, https://web.stanford.edu/group/SOL/software/cgls/, 2015.
[19]

P. Schniter, Low-complexity equalization of OFDM in doubly selective channels, IEEE Trans. Signal Process., vol. 52, no. 4, pp. 1002–1011, 2004.

[20]
K. Hayami, Convergence of the conjugate gradient method on singular systems, arXiv preprint arXiv: 1809.00793, 2018.
[21]

A. Gupta, S. K. Gupta, M. Rashid, A. Khan, and M. Manjul, Unmanned aerial vehicles integrated HetNet for smart dense urban area, Trans. Emerg. Telecommun. Technol., vol. 33, no. 10, p. e4123, 2022.

[22]

C. Chen, J. Zhang, X. Chu, and J. Zhang, On the deployment of small cells in 3D HetNets with multi-antenna base stations, IEEE Trans. Wireless Commun., vol. 21, no. 11, pp. 9761–9774, 2022.

[23]

T. R. Tejasvi and D. H. Manjaiah, Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi-BRLSTM model, Trans. Emerg. Telecommun. Technol., vol. 33, no. 7, p. e4471, 2022.

[24]
A. Thantharate, V. Walunj, R. Abhishek, and R. Paropkari, Balanced5G—Fair traffic steering technique using data-driven learning in beyond 5G systems, in Proc. 2022 8th Int. Conf. Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2022, pp. 685–691.
[25]
A. Solyman, S. Elgamel, S. Weiss, and J. Soraghan, Hybrid FrFT and FFT based multimode transmission OFDM system based, in Proc. 8th Int. Conf. Electrical Engineering ICEENG 2012, Cairo, Egypt, 2012, pp. 1–12.
[26]

A. A. A. Solyman, H. Attar, M. R. Khosravi, V. G. Menon, A. Jolfaei, V. Balasubramanian, B. Selvaraj, and P. Tavallali, A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices, IEEE Access, vol. 8, pp. 67591–67602, 2020.

[27]

L. Ma, H. Jia, S. Liu, and I. U. Khan, Low-complexity Doppler compensation algorithm for underwater acoustic OFDM systems with nonuniform Doppler shifts, IEEE Commun. Lett., vol. 24, no. 9, pp. 2051–2054, 2020.

[28]
I. Jarin and R. Sharmin, Performance evaluation of SISO OFDM system in the presence of CFO, timing jitter and phase noise for Rayleigh and Rician fading channels, in Proc. 2017 IEEE Region 10 Humanitarian Technology Conf. (R10-HTC), Dhaka, Bangladesh, 2017, pp. 498–501.
[29]

T. K. Sarkar, Z. Ji, K. Kim, A. Medouri, and M. Salazar-Palma, A survey of various propagation models for mobile communication, IEEE Antennas Propag. Mag., vol. 45, no. 3, pp. 51–82, 2003.

[30]

A. F. Molisch, Ultrawideband propagation channels-theory, measurement, and modeling, IEEE Trans. Veh. Technol., vol. 54, no. 5, pp. 1528–1545, 2005.

[31]
A. A. A. Solyman, S. Weiss, and J. J. Soraghan, Low-complexity LSMR equalisation of FrFT-based multicarrier systems in doubly dispersive channels, in Proc. 2011 IEEE Int. Symp. Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, 2011, pp. 461–465.
[32]

Y. Ding, T. N. Davidson, Z. Q. Luo, and K. M. Wong, Minimum BER block precoders for zero-forcing equalization, IEEE Trans. Signal Process., vol. 51, no. 9, pp. 2410–2423, 2003.

[33]

Y. Wu and W. Y. Zou, Orthogonal frequency division multiplexing: A multi-carrier modulation scheme, IEEE Trans. Consumer Electron., vol. 41, no. 3, pp. 392–399, 1995.

[34]

G. Taubock, M. Hampejs, P. Svac, G. Matz, F. Hlawatsch, and K. Grochenig, Low-complexity ICI/ISI equalization in doubly dispersive multicarrier systems using a decision-feedback LSQR algorithm, IEEE Trans. Signal Process., vol. 59, no. 5, pp. 2432–2436, 2011.

[35]
T. Hrycak and G. Matz, Low-complexity time-domain ICI equalization for OFDM communications over rapidly varying channels, in Proc. 2006 40th Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, USA, 2006, pp. 1767–1771.
[36]
H. Han and L. N. Wu, Low complexity LSQR-based block decision feedback equalizer for OFDM systems over rapidly time-varying channels, in Proc. 2010 Int. Conf. Communications and Mobile Computing, Shenzhen, China, 2010, pp. 438–441.
[37]

Y. H. Zhou, F. Tong, and G. Q. Zhang, Distributed compressed sensing estimation of underwater acoustic OFDM channel, Appl. Acoust., vol. 117, pp. 160–166, 2017.

[38]

F. Ding, X. Liu, and J. Chu, Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle, IET Contr. Theory Appl., vol. 7, no. 2, pp. 176–184, 2013.

[39]

M. Zhang, A. Zhang, and Q. Yang, Robust adaptive beamforming based on conjugate gradient algorithms, IEEE Trans. Signal Process., vol. 64, no. 22, pp. 6046–6057, 2016.

[40]

C. Jiang, H. Li, and M. Rangaswamy, On the conjugate gradient matched filter, IEEE Trans. Signal Process., vol. 60, no. 5, pp. 2660–2666, 2012.

[41]

L. Wang and R. C. D. Lamare, Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming, IET Signal Process., vol. 4, no. 6, pp. 686–697, 2010.

[42]

Å. Björck, T. Elfving, and Z. Strakos, Stability of conjugate gradient and Lanczos methods for linear least squares problems, SIAM J. Matrix Anal. Appl., vol. 19, no. 3, pp. 720–736, 1998.

[43]
P. Concus and G. H. Golub, A generalized conjugate gradient method for nonsymmetric systems of linear equations, in Computing Methods in Applied Sciences and Engineering, R. Glowinski and J. L. Lions, eds. Berlin, Germany: Springer, 1976. pp. 56–65.
[44]

M. R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems, J. Res. Natl. Bur. Stand., vol. 49, no. 6, p. 409, 1952.

International Journal of Crowd Science
Pages 3-12
Cite this article:
Ata IHM. CGLS Method for Efficient Equalization of OFDM Systems Under Doubly Dispersive Fading Channels with an Application into 6G Communications in Smart Overcrowded. International Journal of Crowd Science, 2025, 9(1): 3-12. https://doi.org/10.26599/IJCS.2023.9100015
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return