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Research Article |

An Integrated Wavelet–Matched Filter–Adaptive Neural Network Framework for Enhanced Pulse Compression and Noise Reduction in Communication Systems

Author(s): Mohammed Aboud Kadhim1*, Ali Jasim Ghaffoori2, and Ahmed Obaid Aftan3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 4

Publisher : FOREX Publication

Published : 30 December 2025

e-ISSN : 2347-470X

Page(s) : 971-985

DOI: https://doi.org/10.37391/IJEER.130437

Abstract

In modern communication systems, it is of great challenge to protect signal from being corrupted by heavy noise. We propose a four-stage overall integrated framework with wavelet-based denoising, matched filtering, adaptive compensation and DNN enhancement for pulse compression and de-noising in this paper. The applicability of the proposed method shows 18.5±2.1% reduction in MSE at 10 dB SNR, across both synthetic rectangular pulses and radar chirp signals as well as biomedical ECG waveforms. The statistical significance was verified with paired t-test (p < 0.001, n=100 trials). Extensive ablation analysis is carried out, showing that each step of the proposed method can lead to 3-8% performance improvement and the gain contributed by employing neural networks (i.e. processing) is most significant (7.2% MSE reduction). The proposed method shows stable performance under the SNR values 5 to 20 dB and has high robustness over various environmental perturbations by up to 15% multiplicative noise. The computational complex is analyzed and the average-case performance of O (N log N) time can support real-time implementation. Experimental results on benchmark databases demonstrate that the proposed framework achieves better performance than state-of-the-art hybrid techniques such as wavelet-neural pairs and matched-filter-adaptive schemes. This paper fills in gaps in the theory of multi-stage signal enhancement for radar, biomedical and industrial communication systems by presenting a full set of architectural details, offering several mathematical formulations and supplying reproducible experimental protocols.

Keywords: Pulse compression, Noise reduction, Wavelet transform, Matched filter, Adaptive filtering, Neural networks, Signal enhancement, Communication systems.




Mohammed Aboud Kadhim*, Polytechnic College for Engineering, Middle Technical University, Baghdad, Iraq; Email: mohammed_aboud@mtu.edu.iq

Ali Jasim Ghaffoori, Al-Ma’moon University College, Baghdad, Iraq

Ahmed Obaid Aftan, Electrical Engineering Technical Colleges, Middle Technical University, Baghdad, Iraq

    [1] Richards, Mark A. Fundamentals of radar signal processing. Vol. 1. New York: Mcgraw-hill, 2005.
    [2] Goldsmith, Andrea. Wireless communications. Cambridge university press, 2005.
    [3] Rangayyan, R. M., & Krishnan, S. (2024). Biomedical signal analysis. John Wiley & Sons.
    [4] Åström, K. J., & Murray, R. (2021). Feedback systems: an introduction for scientists and engineers.Princeton university press.
    [5] Sklar, B. (2021). Digital communications: fundamentals and applications. Pearson.
    [6] Sundararajan, D. (2016). Discrete wavelet transforms: a signal processing approach. John Wiley & Sons.
    [7] Van Trees, H. L., & Bell, K. L. (2013). Detection estimation and modulation theory, part I: detection, estimation, and filtering theory. John Wiley & Sons.
    [8] Farhang-Boroujeny, B. (2013). Adaptive filters: theory and applications. John wiley & sons.
    [9] Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the american statistical association, 90(432), 1200-1224.
    [10] Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7), 3142-3155.
    [11] M. I. Skolnik, Radar Handbook, 3rd ed. McGraw-Hill, 2008.
    [12] Vandewalle, P., Kovacevic, J., & Vetterli, M. (2009). Reproducible research in signal processing. IEEE Signal Processing Magazine, 26(3), 37-47.
    [13] Ramakrishnan, S. (2024). Introductory chapter: Wavelet theory and modern applications. In Modern Applications of Wavelet Transform. IntechOpen.
    [14] Joy, J., Peter, S., & John, N. (2013). Denoising using soft thresholding. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(3), 1027-1032.
    [15] Kingsbury, N. (2001). Complex wavelets for shift invariant analysis and filtering of signals. Applied and computational harmonic analysis, 10(3), 234-253.
    [16] NJ, R. K., & Ghosh, S. K. (2024, March). Denoising of PCG signals: A comparative study of DWT and adaptive based algorithms. In 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (pp. 1-6). IEEE.
    [17] Wu, Z. T., & Hung, J. W. (2025). Improving the Speech Enhancement Model with Discrete Wavelet Transform Sub-Band Features in Adaptive FullSubNet. Electronics, 14(7), 1354.
    [18] Liu, K., Zheng, Q., Tao, K., Li, Z., Qin, H., Li, W., ... & Yang, X. (2025). Low-bit model quantization for deep neural networks: A survey. arXiv preprint arXiv:2505.05530.
    [19] Richards, M. A., Scheer, J. A., & Holm, W. A. (Eds.). (2010). Principles of modern radar: basic principles. SciTech Publishing Inc.
    [20] Cook, C. (2012). Radar signals: An introduction to theory and application. Elsevier.
    [21] Hao, C., Orlando, D., Liu, J., & Yin, C. (2022). Advances in Adaptive Radar Detection and Range Estimation (pp. 1-22). Singapore: Springer.
    [22] Purnima, S., Sathiya, A., Poonkuzhali, P., Balakrishnan, M., & Saravanan, M. (2025, January). Optimizing Neural Prosthetic Control Systems Using Genetic Algorithm for Adaptive Signal Processing. In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) (pp. 559-565). IEEE.
    [23] Zhai, H. Q., & Zhang, J. (2025). Research on the construction of intelligent navigation brain empowered by digital technology. In Expanding Navigation Application and Empowering the Future of Humanity (pp. 61-68). CRC Press.
    [24] James, M. (2025). Adaptive Signal Filtering Algorithms for Enhanced Accuracy in Biosensor Measurement.
    [25] Saravanan, V., Santhiyakumari, N., Thangavel, M., & Hemalatha, R. (2025). Dynamic step-size normalized LMS algorithm for alpha-stable impulsive noise control and peak tracking. Signal, Image and Video Processing, 19(7), 565.
    [26] Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., & Zhang, L. (2025). Deep learning-based software engineering: progress, challenges, and opportunities. Science China Information Sciences, 68(1), 111102.
    [27] Zhu, Y., Zhang, K., Liang, J., Cao, J., Wen, B., Timofte, R., & Van Gool, L. (2023). Denoising diffusion models for plug-and-play image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1219-1229).
    [28] Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
    [29] Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261.
    [30] Bhatnagar, G., Wu, Q. J., & Liu, Z. (2013). Directive contrast based multimodal medical image fusion in NSCT domain. IEEE transactions on multimedia, 15(5), 1014-1024.
    [31] Rabbani, H. (2009). Image denoising in steerable pyramid domain based on a local Laplace prior. Pattern Recognition, 42(9), 2181-2193.
    [32] Wang, Z. S., Li, Y. X., Su, W. Q., Li, Z. S., & Zhang, J. F. (2022). Comparisons of five least-squares adaptive matched filtering methods in multiple suppression. Journal of Geophysics and Engineering, 19(5), 1046-1063.
    [33] Jiang, X., Sun, X., Hao, Y., Cai, Q., Hou, H., Xia, L., & Gao, Z. (2025). WNOTNet: A Hybrid Wavelet Neural Operator and Transformer Framework for Enhanced EEG Denoising. IEEE Transactions on Instrumentation and Measurement.
    [34] Campagne, J. E. (2024). Notes and Comments on S. Mallat’s Lectures at Collège de France (2024) (Doctoral dissertation, Collège de France).
    [35] Glassner, A. (2024). An Introduction to the Fourier Transform. In ACM SIGGRAPH 2024 Courses (pp. 1-89).
    [36] LINCOLN, M. (2001). Laboratory. DARPA intrusion detection evaluation.
    [37] Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 101(23), e215-e220.

Mohammed Aboud Kadhim, Ali Jasim Ghaffoori, Ahmed Obaid Aftan (2025), An Integrated Wavelet–Matched Filter–Adaptive Neural Network Framework for Enhanced Pulse Compression and Noise Reduction in Communication Systems. IJEER 13(4), 971-985. DOI: 10.37391/IJEER.130437.