A Novel Geometry-Aware GPR-Based Energy-Efficient and Low-Overhead Channel Estimation Scheme

In this work, we model the wireless channel as a complex-valued Gaussian process (GP) over the transmit and receive antenna arrays. The channel covariance is characterized using an antenna-geometry-based spectral mixture covariance function (GB-SMCF), which captures the spatial structure of the antenna arrays. To address the problem of accurate channel state information (CSI) estimation from very few noisy observations, we develop a Gaussian process regression (GPR)-based channel estimation framework that employs the GB-SMCF as a prior covariance model with online hyperparameter optimization. In the proposed scheme, the full channel is learned by transmitting pilots from only a small subset of transmit antennas while receiving them at all receive antennas, resulting in noisy partial CSI at the receiver. These limited observations are then processed by the GPR framework, which updates the GB-SMCF hyperparameters online from incoming measurements and reconstructs the full CSI in real time. Simulation results demonstrate that the proposed GB-SMCF-based estimator outperforms baseline methods while reducing pilot overhead and training energy by up to 50$\%$ compared to conventional schemes.