Enhancing Communication Signal Quality through Digital Signal Processing–based Noise Reduction
DOI:
https://doi.org/10.46610/JoAESP.2026.v03i02.004Keywords:
Adaptive filtering, Communication, Signal enhancement, Signal quality, Wireless communicationAbstract
Communication systems are increasingly vulnerable to signal degradation due to noise from multiple sources, including thermal fluctuations, electromagnetic interference, and channel distortions. Noise adversely affects the integrity and reliability of transmitted data, leading to higher bit error rates, reduced Signal-to-Noise Ratio (SNR), and compromised audio or data quality. To address these challenges, Digital Signal Processing (DSP)–based noise reduction techniques have emerged as a vital solution for enhancing communication signal quality across modern wireless, satellite, and wired networks. This study focuses on the application of DSP algorithms for noise mitigation in communication systems. Core techniques examined include linear and adaptive filtering, Fourier transform–based spectral analysis, wavelet denoising, and adaptive noise cancellation. These methods enable the selective attenuation of unwanted noise components while preserving the integrity of the original signal. The study demonstrates how preprocessing, frequency-domain transformations, and adaptive feedback mechanisms collectively improve signal clarity and overall system performance. Experimental and simulation results indicate that DSP-based noise reduction significantly enhances communication quality. Metrics such as SNR, bit error rate, and perceptual audio quality show measurable improvements post-processing. Comparative analysis of noisy versus processed signals illustrates that DSP algorithms effectively isolate desired signal components while suppressing noise, ensuring reliable transmission even in challenging channel conditions. Furthermore, the integration of DSP-based noise reduction facilitates real-time processing and scalability in diverse communication environments, from mobile networks to satellite links. The study underscores the practical implications of adopting DSP methods in modern communication infrastructure, highlighting improvements in reliability, efficiency, and user experience. In conclusion, DSP-based noise reduction represents a critical advancement in communication technology, offering robust, adaptive, and efficient solutions for mitigating noise-related degradation. By leveraging advanced DSP algorithms, communication systems can maintain high-quality signal transmission, reduce errors, and enhance operational performance across varied applications.
References
L. Peng, S. Fang, Y. Fan, M. Wang, and Z. Ma, “A method of noise reduction for radio communication signal based on RaGAN,” Sensors, vol. 23, no. 1, Jan. 2023.
S. Beheshti, A. Fakhrzadeh, and S. Krishnan, “Noiseless codelength in wavelet denoising,” EURASIP Journal on Advances in Signal Processing, vol. 2010, Feb. 2010.
C. Shraddha, M. L. Chayadevi, and M. A. Anusuya, “Biorthogonal wavelet packet and adaptive filters for noisy speech reduction,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26731–26740, Oct. 2025.
M. Vaithianathan, “Digital signal processing for noise suppression in voice signals,” International Journal of Advanced Research in Interdisciplinary Science, vol. 1, no. 4, pp. 198–208, 2024.
M. Z. Rahman A M J, C. Mathew O, B. G, S. K, and N. P, “Emerging digital signal processing techniques for enhanced communication systems,” International Journal of Environmental Sciences, vol. 11, no. 1, pp. 826–837, Jul. 2025.
C. A. Prasath, “Adaptive filtering techniques for real-time audio signal enhancement in noisy environments,” National Journal of Signal and Image Processing, vol. 1, no. 1, pp. 26–33, 2025.
S. A. Irfani, “Audio signal extraction in noisy environment using DSP technique,” International Journal of Engineering Research and Technology, vol. 4, no. 8, pp. 484–487, Aug. 2015.
S. Rani and R. Kaur, “Review: Audio noise reduction using filters and discrete wavelet transformation,” Journal of Advanced Research in Electrical and Electronic Engineering, vol. 2, no. 6, pp. 17–21, Jun. 2025.
J. Gao, H. Sultan, J. Hu and W. -W. Tung, “Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison,” in IEEE Signal Processing Letters, vol. 17, no. 3, pp. 237–240, Mar. 2010.
H. Y. Alzaq and B. B. Ustundag, “Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network,” EURASIP Journal on Wireless Communications and Networking, vol. 2017, Jul. 2017.
M. V. Sudhakar, M. P. Charan, G. N. Pranai, L. Harika, and P. Yamini, “Audio signal noise cancellation with adaptive filter techniques,” Materials Today: Proceedings, vol. 80, pp. 2956–2963, 2023.
T. S. R. Krishna Prasad, D. Praneetha, A. Mavya Sri, G. Vanitha, and Ch. Jaswanth, “Real time speech signal denoising using wavelet thresholding and LMS adaptive filtering,” International Journal of All Research Education & Scientific Methods, vol. 13, no.4, Apr. 2025.
S. Rani and R. Kaur, “Review: Audio noise reduction using filters and discrete wavelet transformation,” Journal of Advance Research in Electrical & Electronics Engineering, vol. 2, no. 6, pp. 19–23, Jun. 2015.
G. Renisha and T. Jayasree, “Enhancement of speech signals in a noisy environment based on wavelet based adaptive filtering,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 9, pp. 69–76, Sep. 2015.
Y. Ghanbari and M. R. Karami-Mollaei, “A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets,” Speech Communication, vol. 48, no. 8, pp. 927–940, Aug. 2006.
P. D. Swami, R. Sharma, A. Jain, and D. K. Swami, “Speech enhancement by noise driven adaptation of perceptual scales and thresholds of continuous wavelet transform coefficients,” Speech Communication, vol. 70, pp. 1–12, Jun. 2015.
D. P. Jarrett, E. A. P. Habets, and P. A. Naylor, “Introduction,” in Theory and Applications of Spherical Microphone Array Processing, Springer Topics in Signal Processing, vol. 9, Cham, Switzerland: Springer, 2017, ch. 1, pp. 1–10.
V. Singh and S. Singh, “Audio noise reduction using discrete wavelet transformation,” International Journal of Engineering Research & Technology, vol. 4, no. 7, pp. 489–492, Jul 2015.
S.-Y. Lung, “Wavelet feature domain adaptive noise reduction using learning algorithm for text-independent speaker recognition,” Pattern Recognition, vol. 40, no. 9, pp. 2603–2606, Sept. 2007.
F. O. S. Gama, L. F. Q. Silveira, and A. O. Salazar, “Adaptive wavelet coding applied in a wireless control system,” Sensors, vol. 17, no. 12, Dec. 2017.
M. T. Johnson, X. Yuan, and Y. Ren, “Speech signal enhancement through adaptive wavelet thresholding,” Speech Communication, vol. 49, no. 2, pp. 123–133, Feb. 2007.
E. Ö. Acarbay and N. Özkurt, “Performance analysis of the speech enhancement application with wavelet transform domain adaptive filters,” International Journal of Speech Technology, vol. 26, pp. 245–258, Mar. 2023.
