Low-Overhead CSI Prediction via Gaussian Process Regression

TL;DR

This paper introduces a Gaussian process regression framework for predicting full channel state information (CSI) from limited observations, significantly reducing pilot overhead in multi-antenna systems while maintaining accuracy.

Contribution

It develops a novel GPR-based CSI estimation method using multiple kernels to model spatial correlations, enabling efficient CSI prediction with reduced pilot overhead.

Findings

GPR with 50% pilot saving achieves lowest prediction error.

The approach provides high credible-interval coverage.

It preserves spectral efficiency better than benchmarks.

Abstract

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as antenna counts grow. In this paper, we address this challenge by developing a novel framework based on Gaussian process regression (GPR) that predicts full CSI from only a few observed entries, thereby reducing pilot overhead. The correlation between data points in GPR is defined by the covariance function, known as kernel. In the proposed GPR-based CSI estimation framework, we incorporate three kernels, i.e., radial basis function, Mat'ern, and rational quadratic, to model smooth and multi-scale spatial correlations derived from the antenna array geometry. The proposed approach is evaluated across two channel models with three distinct pilot probing schemes. Results show that the proposed GPR with 50% pilot saving achieves the lowest prediction error, the highest empirical 95% credible-interval coverage, and the best preservation of spectral efficiency relative to the benchmarks.