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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Cross-correlation-based signal pruning method (CCSPM) for effective signal distortion reduction in massive MIMO

M Kasiselvanathan1, SatheeshKumar Palanisamy2, N Sathishkumar3

  • 1Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, 641022, Tamilnadu, India.

Scientific Reports
|July 2, 2025
PubMed
Summary

Massive MIMO systems face signal distortion from overlapping channels. A new Cross Correlation-based Signal Pruning Method (CCSPM) effectively reduces this distortion and extraction complexity for better performance.

Keywords:
Linear learningMIMOSignal pruningVector representation

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Telecommunications Engineering

Background:

  • Massive Multiple-Input-Multiple-Output (MIMO) enhances spectral and energy efficiency in base stations.
  • Signal distortion arises from overlapping channels adapting to receiver radiation patterns.

Purpose of the Study:

  • To introduce a Cross Correlation-based Signal Pruning Method (CCSPM) for suppressing signal distortion.
  • To reduce extraction complexity at the receiver terminal in massive MIMO systems.

Main Methods:

  • CCSPM identifies correlations using cumulative distortion rates between adjacent channels during beamforming.
  • Correlation is pruned until a clear channel is established between base station and receiver.
  • Linear vector learning validates the process, pursuing signal vector representations and pruning for minimal distortion.

Main Results:

  • The CCSPM method improves interval output by 9.39%.
  • It reduces signal distortion rate by 7.9%.
  • Complexity is reduced by 9.09% across varying frequencies.

Conclusions:

  • CCSPM effectively suppresses signal distortion in massive MIMO systems.
  • The method significantly reduces extraction complexity and improves signal quality.
  • This contributes to more efficient and reliable wireless communication.