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Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
Published on: June 25, 2021
Statistical mechanical analysis of the Kronecker channel model for multiple-input multiple-output wireless
Atsushi Hatabu1, Koujin Takeda, Yoshiyuki Kabashima
1System IP Core Research Laboratories, NEC Corporation, Kawasaki 211-8666, Japan.
Summary
This study analyzes the Kronecker channel model in wireless communication using statistical mechanics. A new formalism handles antenna correlations, showing approximations are valid for small correlation strengths.
Area of Science:
- Wireless Communication
- Statistical Mechanics
- Information Theory
Background:
- The Kronecker channel model is crucial for analyzing wireless communication systems.
- Spatial antenna correlations introduce complex dependencies, hindering performance assessment.
- Existing analytical schemes struggle with these nontrivial symbol dependencies.
Purpose of the Study:
- To develop a formalism for analyzing Kronecker channel models with spatial antenna correlations.
- To assess the impact of correlation strength on communication performance.
- To provide an accurate performance evaluation method for wireless systems.
Main Methods:
- Application of statistical mechanics methods to the Kronecker channel model.
- Development of a new formalism to handle correlation matrices.
- Mapping performance analysis to one-dimensional spin systems in random fields for specific correlations.
Main Results:
- The developed formalism formally handles correlations in Kronecker models.
- Correlation effects generally increase after the fourth order with respect to correlation strength.
- The known analytical scheme provides a good approximation for small correlation strengths.
Conclusions:
- The developed formalism is valuable for understanding correlation effects in wireless communication.
- Performance analysis can be approximated effectively when correlation strength is small.
- Belief propagation algorithms can analyze specific correlation cases without approximation.
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