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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.
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.
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.
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