Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

Submit Your Paper

2690-6775ISSN (Online) 2769-7851ISSN (Print)

Volume 15 , Issue 1 , PP: 211-224, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems

Nallamothu Suneetha 1 * , Penke Satyanarayana 2

  • 1 Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India; Department of ECE, Sir C R Reddy College of Engineering, Eluru, India - (suneethavelamati@gmail.com)
  • 2 Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India - (satece@kluniversity.in)
  • Doi: https://doi.org/10.54216/JCIM.150116

    Received: April 12, 2024 Revised: June 10, 2024 Accepted: August 06, 2024
    Abstract

    Channel estimation poses critical challenges in millimeter-wave (mmWave) massive Multiple Input, Multiple Output (MIMO) communication models, particularly when dealing with a substantial number of antennas. Deep learning techniques have shown remarkable advancements in improving channel estimation accuracy and minimizing computational difficulty in 5G as well as the future generation of communications. The main intention of the suggested method is to use an optimal hybrid deep learning strategy to create a better channel estimation model. The proposed method, referred to as optimized D-LSTM, combines the power of a deep neural network (DNN) and long short-term memory (LSTM), and the optimization process involves the integration of the Reptile Search Algorithm (RSA) to enhance the performance of  deep learning model. The suggested hybrid deep learning method considers the correlation between the measurement matrix and the signal vectors that were received as input to predict the amplitude of the beam space channel. The newly proposed estimation model demonstrates remarkable superiority over traditional models in both Normalized Mean-Squared Error (NMSE) reduction and enhanced spectral efficiency. The spectral efficiency of the designed RSA-D-LSTM is 68.62%, 62.26%, 30.3%, and 19.77% higher than DOA, DHOA, HHO, and RSA. Therefore, the suggested system provides better channel estimation to improve its efficiency.

    Keywords :

    Millimeter-wave (mmWave) , Massive multi-input multi-output (MIMO) , Deep neural network (DNN) , Long short-term memory (LSTM) , Reptile search algorithm (RSA)

    References

     [1]    J. Du, M. Han, Y. Chen, L. Jin, and F. Gao, "Tensor-Based Joint Channel Estimation and Symbol Detection for Time-Varying mmWave Massive MIMO Systems," IEEE Transactions on Signal Processing, vol. 69, pp. 6251-6266, 2021.

    [2]     S. Lyu, X. Li, T. Fan, J. Liu, and M. Shi, "Deep learning for fast channel estimation in millimeter-wave MIMO systems," Journal of Systems Engineering and Electronics, vol. 33, no. 6, pp. 1088-1095, December 2022.

    [3]    X. Ma, Z. Gao, F. Gao, and M. Di Renzo, "Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter Wave Massive Hybrid MIMO Systems," IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2388-2406, Aug. 2021.

    [4]   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, Jan. 2021.

    [5]   G. Zhou, C. Pan, H. Ren, P. Popovski and A. L. Swindlehurst, "Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems," IEEE Transactions on Signal Processing, vol. 70, pp. 1478-1492, 2022.

    [6]    S. -G. Yoon and S. J. Lee, "Improved Hierarchical Codebook-Based Channel Estimation for mmWave Massive MIMO Systems," IEEE Wireless Communications Letters, vol. 11, no. 10, pp. 2095-2099, Oct. 2022.

    [7]    X. Wu, X. Yang, S. Ma, B. Zhou and G. Yang, "Hybrid Channel Estimation for UPA-Assisted Millimeter-Wave Massive MIMO IoT Systems," IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2829-2842, 15 Feb.15, 2022.

    [8]     R. Jiang, X. Wang, S. Cao, J. Zhao, and X. Li, “Deep neural networks for channel estimation in underwater acoustic OFDM systems,” IEEE Access, vol. 7, 2019. Wireless Communications and Mobile Computing 9

    [9]    M. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, “Deep learning-based channel estimation,” IEEE Communications Letters, vol. 23, no. 4, pp. 652–655, 2019.

    [10]    L. Li, H. Chen, H.-H. Chang, and L. Liu, “Deep residual learning meets OFDM channel estimation,” IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 615–618, 2020.

    [11]   M. Alweshah, “Solving feature selection problems by combining mutation and crossover operations with the monarch butterfly optimization algorithm,” Applied Intelligence, vol. 51, no. 6, pp. 4058–4081, 2021.

    [12]    M. Alweshah, S. A. Khalaileh, B. B. Gupta, A. Almomani, A. I. Hammouri, and M. A. Al-Betar, “)e monarch butterfly optimization algorithm for solving feature selection problems,” Neural Computing & Applications, vol. 34, no. 14, pp. 11267–11281, 2020.

    [13]    X. K. Lin and Y. H. Wu, “Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture,” Energy, vol. 196, Article ID 117054, 2020.

    [14]    Z. Xu, H. C. Yang, J. Y. Li, X. Y. Zhang, B. Lu, and S. C. Gao, “Comparative study on single and multiple chaotic maps incorporated grey wolf optimization algorithms,” IEEE Access, vol. 9, pp. 77416–77437, 20

    [15]    L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi, “Reptile search algorithm (RSA): a nature inspired meta-heuristic optimizer,” Expert Systems with Applications, vol. 191, Apr. 2022, Article ID 116158, doi: 10.1016/j.eswa.2021.116158

    [16]   Ma W, Qi C, Zhang Z, Cheng J. Deep Learning for Compressed Sensing Based Channel Estimation in Millimeter Wave Massive MIMO. International Conference on Wireless Communications and Signal Processing (WCSP); 2019.

    [17]    Z. Yi and W. Zou, "A Novel NE-DFT Channel Estimation Scheme for Millimeter-Wave Massive MIMO Vehicular Communications," IEEE Access, vol. 8, pp. 74965-74976, 2020.

    [18]   Manasa BMR, Venugopal P. Swarm intelligence-based deep ensemble learning machine for efficient channel estimation in MIMO communication systems. Commun Syst. 2022;35(10)

    [19]    Y. Song, Z. Gong, Y. Chen and C. Li, "Efficient Channel Estimation for Wideband Millimeter Wave Massive MIMO Systems with Beam Squint," IEEE Transactions on Communications, vol. 70, no. 5, pp. 3421-3435, May 2022

    [20]    Rong Dai, Yang Liu, Qin Wang, Yu Yu, Xin Guo, "Channel estimation by reduced dimension decomposition for millimeter wave massive MIMO system," Elsevier Physical Communication, Vol. 44, no.101241, February 2021.

    [21]   Raj S, Gilbert M, Bala GJ. Millimeter-wave massive MIMO channel estimation based on majorization–minimization approach. Phys Commun. 2021;47(101385)

    [22]   Ghobaei-Arani M, Shahidinejad A. An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput. 2021; 77:711-750

    [23]    Nallamothu Suneetha and Penke Satyanarayana, "Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement," Wiley, 2022, doi.org/10.1002/dac.5400.

    [24]    Heath RW, Gonzalez-Prelcic N, Rangan S, Roh W, Sayeed AM. An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Topics Signal Process. 2016;10(3):436-453. doi:10.1109/JSTSP.2016.2523924

    [25]    Ha AL, Van Chien T, Nguyen TH, Choi W, Nguyen VD. Deep Learning-Aided 5G Channel Estimation. 15th International Conference on Ubiquitous Information Management and Communication (IMCOM); 2021:1-7

    [26]   Le HA, Van Chien T, Nguyen TH, Choo H, Nguyen VD. Machine learning-based 5G-and-beyond channel estimation for MIMO-OFDM communication systems. Sensors. 2021;21(4861):4861. doi:10.3390/s21144861

    [27]   Srinivasa Rao Y, Madhu R. Channel estimation for millimeter wave massive MIMO system: proposed hybrid optimization with heuristic-enabled precoding and combining. Comput J. 2021;65(5):1211-1224

    [28]   Srinivasa Rao Y, Ramarakula M. Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system. Int J Speech Technol. 2021;24(5):315-327. doi:10.1007/s10772-021-09798-z

    [29]   AsgharHeidari A, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: algorithm and applications. Future Gener Comput Syst. 2019; 97:849-872. doi:10.1016/j.future.2019.02.028

     

    Cite This Article As :
    Suneetha, Nallamothu. , Satyanarayana, Penke. A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 211-224. DOI: https://doi.org/10.54216/JCIM.150116
    Suneetha, N. Satyanarayana, P. (2025). A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems. Journal of Cybersecurity and Information Management, (), 211-224. DOI: https://doi.org/10.54216/JCIM.150116
    Suneetha, Nallamothu. Satyanarayana, Penke. A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems. Journal of Cybersecurity and Information Management , no. (2025): 211-224. DOI: https://doi.org/10.54216/JCIM.150116
    Suneetha, N. , Satyanarayana, P. (2025) . A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems. Journal of Cybersecurity and Information Management , () , 211-224 . DOI: https://doi.org/10.54216/JCIM.150116
    Suneetha N. , Satyanarayana P. [2025]. A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems. Journal of Cybersecurity and Information Management. (): 211-224. DOI: https://doi.org/10.54216/JCIM.150116
    Suneetha, N. Satyanarayana, P. "A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems," Journal of Cybersecurity and Information Management, vol. , no. , pp. 211-224, 2025. DOI: https://doi.org/10.54216/JCIM.150116