Noise Cancellation in Communication Systems using LMS and RLS Algorithms
DOI:
https://doi.org/10.69996/fmep.2026007Keywords:
Noise Cancellation, Adaptive Filter, LMS Algorithm, RLS Algorithm, Communication, SystemAbstract
A review of noise cancellation in communication systems based on adaptive filter algorithms is presented. The modifying of signal characteristics in noise cancellation is relatively fast which involves the utilization of adaptive algorithms for quick coverage. The LMS and RLS algorithms play a vital role in noise cancellation to increase the convergence rate. Current communication applications require reducing the computational complexity in real-time implementation. The category of adaptive filters is automatically changing the parameters of its algorithms according to the input signal. Many adaptive filters are digitally executed to update the algorithm parameters. The original applications of an adaptive filter in numerous fields including system recognition and noise cancellation can be modeled by MATLAB simulator. The input signal of an adaptive filter is with the mixed algorithms and noise signal in the specific design of MATLAB to remove the noise from the original data and produce reliable data at the output port. The filter implementation shows the strength of the suggested structure and the capabilities of the proposed algorithms have been investigated and analyzed. The performance of the filter is promising to support the current and future communication systems in terms of noise cancellation.
References
[1] S.Haykin, “Adaptive Filter Theory,” PrenticeHall,2002.
[2] B.Widrow and S.Stearns, “Adaptive SignalProcessing,” PrenticeHall,1985.
[3] S.K.Mitra, “Digital Signal Processing,” McGrawHill,2006.
[4] A.V.OppenheimandR.Schafer, “DiscreteTimeSignalProcessing,” PrenticeHall, 2010.
[5] M.H.Hayes, “Statistical Digital Signal Processing and Modeling,” Communication Engineering Research (IJCCER), vol. 1,no. 4, 2013.
[6] Shubhra Dixit and Deepak Nagaria, “LMS Adaptive Filters for Noise Cancellation: A Review,” International Journal of Electrical and Computer Engineering(IJECE), vol.7, no.5, pp.2520-2529, 2017.
[7] Prashant Kumar, “A Hybrid Approach: Combining Genetic Algorithms and Machine Learning for Function Optimization,” JCESH, vol. 2, no. 1, pp. 17–29, 2025
[8] S.A. Prasetyowati and A. Susanto,“Multiple Processes for Least MeanSquare Adaptive Algorithm on Roadway Noise Cancelling,” International Journal of Electrical and Computer Engineering (IJECE),vol.5, no.2, pp.355, 2015.
[9] J. Mohammed, “A study on the suitability of genetic algorithm for adaptive channel equalization,” International Journal of Electrical andComputer Engineering (IJECE),vol.2, no.3, pp.285, 2012.
[10] J.R. Mohammed, “Low complexity adaptive noise canceller for mobile phones based remote health monitoring,” International Journalof Electrical and Computer Engineering (IJECE), vol.4, no.3, pp.422, 2014
[11] M DixitShubhra and Nagaria Deepak,“Design and analysis ofCascaded LMS adaptive Filters for Noise Cancellation,” Circuits Systems and Signal Processing,vol.36, no.2, 2017.
[12] B. Paul and P. Mythili, “ECG noise removal using GA tuned sign-data least meansquare algorithm,” Advanced Communication Control and Computing Technologies (ICACCCT), pp. 100-103, 2012.
