Biologically Inspired Pattern Recognition for Robust MIMO Signal Detection Using Convolutional Neural Architectures

Abstract

Modern MIMO (Multiple-Input Multiple-Output) signal detection faces increasing challenges due to high-dimensional interference, nonlinear channel effects, and noise uncertainty. Inspired by biological signal processing mechanisms observed in neural, sensory, and genetic systems, this work explores convolution-based modeling and neural network architectures for efficient MIMO detection within a pattern recognition framework. In this context, received MIMO signals are treated as high-dimensional patterns, and detection is formulated as a nonlinear classification and decision-boundary adaptation problem. Biological systems inherently perform parallel convolution, distributed feature extraction, adaptive filtering, and noise-robust pattern discrimination properties that closely resemble MIMO communication channels. By mathematically modeling these biological processes using convolution operators, stochastic difference equations, and adaptive learning dynamics, this study establishes formal analogies between synaptic transmission, sensory coding, genetic regulatory networks, and pattern-based MIMO detection frameworks. Theoretical guarantees on convergence, stability, and error minimization are derived using optimization theory, probabilistic bounds, and Lipschitz-based stability analysis. The proposed biologically inspired pattern recognition models exhibit enhanced robustness to noise, scalable detection in large antenna systems, and adaptive decision boundary formation, making them well suited for next-generation wireless communication systems.

KEYWORDS 

MIMO Signal Detection, Biological Pattern Recognition, Neural Network Models, Convolution Operators, Adaptive Decision Boundaries, Noise-Robust Classification