Home / Current Issue / Paper 1722941
Noise Reduction in Differential Phase Shift Keying Modulator Using Recursive Least Square Filter
Subject area: Science,Engineering and Technology · Area of research: Recursive Least Square Filter
DOI: 10.64388/IREV10I3-1722941
Abstract
This study investigates the performance enhancement of Differential Phase Shift Keying (DPSK) communication systems under noisy channel conditions using an adaptive filtering method, specifically the Recursive Least Squares (RLS) approach. The primary objective is to mitigate interference and improve signal integrity in real-time applications where additive white Gaussian noise (AWGN) degrades performance. DPSK signals were mathematically modelled, considering phase shifts of 0° and 180° for binary encoding, and transmitted through simulated noisy channels with input SNRs ranging from 0 to 20 dB. A model-based design framework in MATLAB was employed to implement the RLS filter, which recursively updates its coefficients to minimise the mean square error between the received and estimated signals. Performance was quantitatively evaluated using Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), and spectral efficiency metrics. Results indicate that the RLS filter achieved a maximum noise reduction of 8 dB at 0 dB input SNR and consistently outperformed conventional Butterworth filters. At a critical SNR of 10 dB, the DPSK-RLS system reduced noise by 75%, compared to 25.2% and 41.2% reported by Ofeogbu et al. (2022) and Liang et al. (2023), respectively, and increased spectral efficiency from 1.15 to 3.45 bps/Hz. Stability and convergence analyses using Lyapunov functions confirmed reliable real-time adaptation. The proposed method demonstrates a robust and adaptive solution for high-reliability communication systems, ensuring superior error mitigation, improved SNR, and enhanced bandwidth utilisation, providing practical guidance for deploying adaptive filtering in modern digital communication networks. Also adaptive recursive least square filter reduces mean squared errors from 0.25v2 to 0.01v2 significantly outperforming traditional fixed filters like Butterwork.
Keywords
modulator; noise reduction; differential phase; mitigation; signal integrity
References
[1] Ajose SO, Imoize AL, Obiukwu OM. Bit error rate analysis of different digital modulation schemes in orthogonal frequency division multiplexing systems. Nigerian Journal of Technology (NIJOTECH). 2018;37(3):727–734.
[2] Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning; Sydney, Australia. 2017. p. 2642–2651.
[3] Thulasi N, Nagarajan V. Enhancement of noise reduction in phase amplitude signal for combinatorial circuits through DQPSK. IEEE Explore. 2019;32(12):87–95.
[4] Liang B, Shengliang F, Youchen F, Mengtao W, Zhao M. A method of noise reduction for radio communication signal based on RaGAN. Sensors. 2023;23(1):470–475.
[5] Singh P, Pradhan G. A new ECG denoising framework using generative adversarial network. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2020;18(5):759–764.
[6] Soltani AA, El-Hag A. Denoising of radio frequency partial discharge signals using artificial neural network. Energies. 2019;12(18):3485.
[7] Wang H, Wang B, Li Y. IAFNet: Few-shot learning for modulation recognition in underwater impulsive noise. IEEE Communications Letters. 2022;26(7):1047–1051.
[8] Wang M, Lin F, Chen K, Luo W, Qiang S. TEM-NLnet: A deep denoising network for transient electromagnetic signal with noise learning. IEEE Transactions on Geoscience and Remote Sensing. 2022;60(1):1–14.
[9] Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, et al. Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics; Berlin, Germany. 2016. p. 207–212.
[10] Zhu W, Mousavi SM, Beroza GC. Seismic signal denoising and decomposition using deep neural networks. IEEE Transactions on Geoscience and Remote Sensing. 2019;57(12):9476–9488.
How to cite this paper
@article{1722941,
author = {Adegboye Omotooke Sola, B. I. Bakare, F. O. Philip-Kpae},
title = {Noise Reduction in Differential Phase Shift Keying Modulator Using Recursive Least Square Filter},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {4019-4036},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722941.pdf},
abstract = {This study investigates the performance enhancement of Differential Phase Shift Keying (DPSK) communication systems under noisy channel conditions using an adaptive filtering method, specifically the Recursive Least Squares (RLS) approach. The primary objective is to mitigate interference and improve signal integrity in real-time applications where additive white Gaussian noise (AWGN) degrades performance. DPSK signals were mathematically modelled, considering phase shifts of 0° and 180° for binary encoding, and transmitted through simulated noisy channels with input SNRs ranging from 0 to 20 dB. A model-based design framework in MATLAB was employed to implement the RLS filter, which recursively updates its coefficients to minimise the mean square error between the received and estimated signals. Performance was quantitatively evaluated using Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), and spectral efficiency metrics. Results indicate that the RLS filter achieved a maximum noise reduction of 8 dB at 0 dB input SNR and consistently outperformed conventional Butterworth filters. At a critical SNR of 10 dB, the DPSK-RLS system reduced noise by 75%, compared to 25.2% and 41.2% reported by Ofeogbu et al. (2022) and Liang et al. (2023), respectively, and increased spectral efficiency from 1.15 to 3.45 bps/Hz. Stability and convergence analyses using Lyapunov functions confirmed reliable real-time adaptation. The proposed method demonstrates a robust and adaptive solution for high-reliability communication systems, ensuring superior error mitigation, improved SNR, and enhanced bandwidth utilisation, providing practical guidance for deploying adaptive filtering in modern digital communication networks. Also adaptive recursive least square filter reduces mean squared errors from 0.25v2 to 0.01v2 significantly outperforming traditional fixed filters like Butterwork.},
keywords = {modulator; noise reduction; differential phase; mitigation; signal integrity},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1722941}
}