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A novel channel estimation model for broadband wireless communication system using hybrid heuristic-based invariable step-size zero-attracting NLMS algorithm

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Abstract

Channel estimation is a significant constraint for the communication system. The channel estimation in wireless communication systems is carried out by adopting distinct approaches. In addition to this, the more important techniques are Minimum Mean Square Error (MMSE), and Least Square (LS). Here, the LS process for estimating the channels is simpler, but it gets high error regarding Mean Square Error (MSE). On contrary to that, the efficiency of MMSE is more when evaluating with LS with Signal-To-Noise Ratio (SNR). It also has high computational complexity. Therefore, the integration of LS and MMSE approaches are used for receiving the exact signal and reducing the error rate through evolutionary programming. Moreover, the Invariable Step-Size Zero-Attracting Normalized Least Mean Square (ISS-ZA-NLMS) methodology has been adopted for exploiting the channel sparsity in Adaptive Sparse Channel Estimation (ASCE). On the other hand, ISS-ZA-NLMS faces inefficiency in performance in terms of a lack of good trade-off between the computational cost and convergence rate. Hence, the main scope of this research is to estimate a new channel estimation technique in broadband wireless communication systems with the help of a hybridized optimization algorithm. Here, the development of Hybrid Heuristic-based ISS-ZA-NLMS (HH-ISS-NLMS) is the main contribution that could enhance the ASCE. The integrated algorithm Tunicate Swarm-Deer Hunting Optimization (TS-DHO) is proposed for efficiently estimating the channels. The objective function of the advanced method is derived concerning MSE. Finally, results show that the channel estimation by the offered HH-ISS-NLMS algorithm estimated with the other traditional approaches shows enhanced performance in terms of MSE.

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References

  • Alkhateeb, A., El Ayach, O., Leus, G., Heath, R.W.: Channel estimation and hybrid precoding for millimeterwave cellular systems. IEEE Journal of Selected Topics in Signal Processing 8(5), 831–846 (2014)

    Article  Google Scholar 

  • Ambati, L.S., Narukonda, K., Bojja, G.R., Bishop, D.: Factors influencing the adoption of artificial intelligence in organizations-from an employee's perspective. Adoption of AI in organization from employee perspective (2020)

  • Anandkumar, R.: Hybrid fuzzy logic and artificial Flora optimization algorithm-based two tier cluster head selection for improving energy efficiency in WSNs. Peer-to-Peer Networking and Applications (2021). https://doi.org/10.1007/s12083-021-01174-7

    Article  Google Scholar 

  • Bai, Q., Wang, J., Zhang, Y., Song, J.: Deep learning-based channel estimation algorithm over time selective fading channels. IEEE Transactions on Cognitive Commu-Nications and Networking 6(1), 125–134 (2020)

    Article  Google Scholar 

  • Bajwa, W.U., Haupt, J., Sayeed, A.M., Nowak, R.: Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc. IEEE 98(6), 1058–1076 (2010)

    Article  Google Scholar 

  • Brammya, G., Praveena, S., Ninu Preetha, N.S., Ramya, R., Rajakumar, B.R., Binu, D.: Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput. J. (2019). https://doi.org/10.1093/comjnl/bxy133

    Article  Google Scholar 

  • Cheng, X., Liu, D., Wang, C., Yan, S., Zhu, Z.: Deep learning-based channel estimation and equalization scheme for FBMC/OQAM systems. IEEE Wireless Com-Munications Letters 8(3), 881–884 (2019)

    Article  Google Scholar 

  • Gao, F., Cui, T., Nallanathan, A.: On channel estimation and optimal training design for amplify and forward relay networks. IEEE Trans. Wireless Commun. 7(5), 1907–1916 (2008)

    Article  MATH  Google Scholar 

  • Gao, S., Dong, P., Pan, Z., Li, G.Y.: Deep learning based channel estimation for massive MIMO with mixed-resolution ADCs. IEEE Commun. Lett. 23(11), 1989–1993 (2019)

    Article  Google Scholar 

  • Gao, Z., Wu, M., Hu, C., Gao, F., Wen, G., Zheng, D., Zhang, J.: Data-driven deep learning based hybrid beamforming for aerial massive MIMO-OFDM systems with implicit CSI. Signal Processing (2022)

  • Gui, G., Adachi, F.: Improved adaptive sparse channel estimation using least mean square algorithm. EURASIP J. Wirel. Commun. Netw. 204, 1–18 (2013)

    Google Scholar 

  • Gui, G., Peng, W., Li, X., Liu, B., Adachi, F.: Variable-step-size based sparse adaptive filtering algorithm for channel estimation in broadband wireless communication systems. EURASIP J. Wirel. Commun. Netw. (2014a). https://doi.org/10.1186/1687-1499-2014-195

    Article  Google Scholar 

  • Gui, G., Chen, Z., Li, X., Wan, Q., Huang, J., Adachi, F.: Variable is better than invariable: sparse VSS-NLMS algorithms with application to adaptive MIMO channel estimation. Sci. World J. 2014, 1–10 (2014b)

    Google Scholar 

  • He, H., Wen, C., Jin, S., Li, G.: Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wireless Communications Letters 7(5), 852–855 (2018)

    Article  Google Scholar 

  • Jagadeeshwar, T., Kalyani, S., Rajagopal, P., Srinivasan, B.: Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves. Struct. Health Monit. (2021). https://doi.org/10.1177/14759217211073335

    Article  Google Scholar 

  • Josiam, K., Rajan, D.: Bandwidth efficient channel estimation using super-imposed pilots in OFDM systems. IEEE Transaction on Wireless Communications 6(6), 2234–2245 (2007)

    Article  Google Scholar 

  • Kang, J., Chun, C., Kim, I.: Deep-learning-based channel estimation for wireless energy transfer. IEEE Commun. Lett. 22(11), 2310–2313 (2018)

    Article  Google Scholar 

  • Kaur, S., Awasthi, L.K., Sangal, A.L., Dhiman, G.: Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng. Appl. Artif. Intell. 90, 103541 (2020)

    Article  Google Scholar 

  • Li, L., Mao, M., Cao, N., Li, J.: LMMSE channel estimation for wireless energy harvesting AF relaying. Physical Communication 27, 133–142 (2018)

    Article  Google Scholar 

  • Ma, X., Chen, Z., Chen, W., Li, Z., Chi, Y., Han, C., Li, S.: Joint channel estimation and data rate maximization for intelligent reflecting surface assisted terahertz MIMO communication systems. IEEE Access 8, 99565–99581 (2020)

    Article  Google Scholar 

  • Moon, S., Kim, H., You, Y.-H., Kim, C.H., Hwang, I.: Deep neural network for beam and blockage prediction in 3GPP-based indoor hotspot environments. Wireless Pers. Commun. (2022). https://doi.org/10.1007/s11277-022-09513-4

    Article  Google Scholar 

  • Mthethwa, B., Xu, H.: Deep learning-based wireless channel estimation for MIMO uncoded space-time labeling diversity. IEEE Access 8, 224608–224620 (2020)

    Article  Google Scholar 

  • Murthy, C.R., Jagannatham, A.K., Rao, B.D.: Training-based and semi-blind channel estimation for MIMO systems with maximum ratio transmission. IEEE Trans. Signal Process. 54(7), 2546–2558 (2006)

    Article  MATH  Google Scholar 

  • Nawar, N., El-Gayar, O., Ambati, L.S., Bojja, G.R.: Social media for exploring adverse drug events associated with multiple sclerosis. In: Proceedings of the 55th Hawaii International Conference on System Sciences, pp. 4217–4226 (2022)

  • Neha N.K., Kochhar, A.: Efficient channel estimation in mm wave massive MIMO using hybrid beamforming. In: Proceedings of First International Conference on Computational Electronics for Wireless Communications, vol 329, pp. 87–95, (2022)

  • Ouyang, R., Matsumura, T., Mizutani, K., Harada, H.: A reliable channel estimation scheme using scattered pilot pattern for IEEE 802.22-based mobile communication system. IEEE Transactions on Cognitive Communications and Networking 5(4), 935–948 (2019)

    Article  Google Scholar 

  • Őzmen, A., Şenol, H.: Channel estimation for realistic indoor optical wireless communication in ACO-OFDM systems. Wireless Pers. Commun. 102, 247–259 (2018)

    Article  Google Scholar 

  • Raghavendra, M.R., Giridhar, K.: Improving channel estimation in OFDM systems for sparse multipath channels. IEEE Signal Process. Lett. 12(1), 52–55 (2005)

    Article  Google Scholar 

  • Raslan, W.A., Mohamed, M.A., Abdel-Atty, H.M.: Deep-BiGRU based channel estimation scheme for MIMO–FBMC systems. Physical Communication 51, 101592 (2022)

    Article  Google Scholar 

  • Salari, A., Shirvanimoghaddam, M., Shahab, M.B., Arablouei, R., Johnson, S.: Clustering-based joint channel estimation and signal detection for NOMA. Comput. Sci. Inf. Theory 1–30 (2022)

  • Shin, H.-C., Sayed, A.H., Song, W.-J.: Variable step-size NLMS and affine projection algorithms. IEEE Signal Process. Lett. 11(2), 132–135 (2004)

    Article  Google Scholar 

  • Shin, C., Heath, R.W., Powers, E.J.: Blind channel estimation for MIMO-OFDM systems. IEEE Trans. Veh. Technol. 56(2), 670–685 (2007)

    Article  Google Scholar 

  • Soltani, M., Pourahmadi, V., Mirzaei, A., Sheikhza-deh, H.: Deep learning-based channel estimation. IEEE Commun. Lett. 23(4), 652–655 (2019)

    Article  Google Scholar 

  • Sujitha, J., Baskaran, K.: Genetic grey wolf optimizer based channel estimation in wireless communication system. Wireless Pers. Commun. 99, 965–984 (2017)

    Article  Google Scholar 

  • Sulaiman, M.H., Mustaffa, Z., Saari, M.M., Daniyal, H.: Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103330 (2020)

    Article  Google Scholar 

  • Tabjula, J.L., Kanakambaran, S., Kalyani, S., Rajagopal, P., Srinivasan, B.: Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Structural Control and Health Monitering (2021). https://doi.org/10.1002/stc.2690

    Article  Google Scholar 

  • Upadhya, K., Seelamantula, C.S., Hari, K.V.S.: A risk minimization framework for channel estimation in OFDM systems. Signal Process. 128, 78–87 (2016)

    Article  Google Scholar 

  • Wan, Z., Gao, Z., Shim, B., Yang, K., Mao, G., Alouini, M.-S.: Compressive sensing based channel estimation for millimeter-wave full-dimensional MIMO with lens-array. IEEE Trans. Veh. Technol. 69(2), 2337–2342 (2020)

    Article  Google Scholar 

  • Yang, Y., Gao, F., Ma, X., Zhang, S.: Deep learning-based channel estimation for doubly selective fading channels. IEEE Access 7, 36579–36589 (2019)

    Article  Google Scholar 

  • Ye, H., Li, G.Y., Juang, B.: Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters 7(1), 114–117 (2018)

    Article  Google Scholar 

  • Zhao, T., Liu, L., Liu, Le., Zhang, G.: Differential evolution particle filtering channel estimation for non-line-of-sight wireless ultraviolet communication. Optics Communications 451, 80–85 (2019)

    Article  Google Scholar 

  • Zitar, R.A., Al-Betar, M.A., Awadallah, M.A., Doush, I.A., Assaleh, K.: An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Archives of Computational Methods in Engineering (2021). https://doi.org/10.1007/s11831-021-09585-8

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

    R. Iniyavan & B. Vijayalakshmi

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  1. R. Iniyavan
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  2. B. Vijayalakshmi
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Iniyavan, R., Vijayalakshmi, B. A novel channel estimation model for broadband wireless communication system using hybrid heuristic-based invariable step-size zero-attracting NLMS algorithm. Int J Intell Robot Appl 7, 370–384 (2023). https://doi.org/10.1007/s41315-022-00251-1

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