Adaptive Pilot-Assisted Channel Estimation for OFDM-Based High-Speed Railway Communications

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems.