Geometry-Constrained Detection in MIMO Systems: A Feature-Space Perspective
TL;DR
The findings suggest that feature-space geometry provides a useful and interpretable framework for understanding performance trends across different communication scenarios and detector families, while highlighting the influence of symbol separability on achievable detection performance.
Abstract
Detection performance in MIMO communication systems is commonly analyzed in terms of Signal to Noise Ratio (SNR), channel conditions, and detector design. However, the role of post-equalization feature-space geometry in shaping detection behavior remains insufficiently understood. In this paper, a geometry ratio, defined as the ratio between inter-class separation and intra-class dispersion, is introduced to characterize the separability of received symbol representations in feature space. Extensive simulations are conducted across SISO and MIMO configurations using 16-QAM and 64-QAM modulation over AWGN and Rayleigh fading channels with ZF and MMSE equalization. The results show a strong statistical relationship between the proposed geometry ratio and Bit Error Rate (BER) across classical, MMSE-SIC, and learning-based detectors, including SVM and neural-network classifiers. Additional analyses demonstrate that the geometry ratio exhibits substantially stronger association with BER than inter-class separation or intra-class dispersion considered individually. Furthermore, increasing modulation order and MIMO dimensionality are shown to degrade post-equalization feature-space separability, leading to increased class overlap and reduced detection reliability. These findings suggest that feature-space geometry provides a useful and interpretable framework for understanding performance trends across different communication scenarios and detector families, while highlighting the influence of symbol separability on achievable detection performance.