Short-term fluctuations in reference signal received power (RSRP) under high-mobility conditions can repeatedly reset the time-to-trigger (TTT) timer and cause spatial dispersion of Event A3 handover triggers in high-speed railway 5G-R systems. To address this problem, this study develops a physics-driven, Doppler-aware RSRP input-calibration framework that preserves the standardized handover margin (HOM), TTT, Event A3 semantics, and Radio Resource Control (RRC) procedure. The relative residual Doppler shift is derived from train kinematics and base-station geometry and estimated online using a scalar Kalman filter. The resulting estimate is mapped to an orthogonal frequency-division multiplexing frequency-offset-induced equivalent RSRP loss subject to a physical upper bound. Geometry-based detrending, speed-adaptive causal smoothing of the residual component, and a low-weight bounded position-aided correction are subsequently applied to reconstruct the serving- and candidate-cell RSRP inputs. Event-driven simulations covering viaduct and mountainous railway scenarios, together with ablation, Doppler-sensitivity, speed-adaptability, and observation-error analyses, are conducted to evaluate the proposed method. At a train speed of 350 km/h, the method reduces the root-mean-square error of the inter-cell RSRP difference from 2.3449 to 1.9053 dB and decreases the mean handover trigger-location error from 54.79 to 42.35 m, while maintaining a handover success rate of 99.49%, comparable to the 99.29% achieved by conventional Event A3. The results further demonstrate that, under a carrier frequency of 2.1 GHz and a subcarrier spacing of 30 kHz, the overall performance improvement is governed primarily by geometric detrending, speed-adaptive residual smoothing, and bounded position correction, whereas Doppler-loss compensation provides a small but physically consistent correction of the deterministic measurement bias. The proposed framework therefore improves RSRP input quality and handover trigger-location consistency without modifying the standardized handover decision logic.

Short-term fluctuations in reference signal received power (RSRP) can repeatedly reset the time-to-trigger (TTT) timer and spatially disperse Event A3 handover triggers in high-speed railway 5G-R systems. This study presents a synchronization-aware, physically bounded calibration layer for the RSRP inputs supplied to the standard handover procedure. The method distinguishes the geometric inter-cell Doppler difference from the receiver-side residual frequency offset after synchronization and tracking. Doppler information is therefore used primarily as a physical prior for residual-frequency estimation and as the basis for a small, bounded useful-component correction, which is verified by direct CP-OFDM/FFT calculation. Geometry-based detrending, speed-adaptive causal residual smoothing with a persistent-attenuation guard, and a bounded position-difference prior are then applied without modifying HOM, TTT, Event A3, or RRC procedures. Event-driven Monte Carlo simulations use common random numbers and include propagation-model mismatch, localized blockage, and Doppler-observation-error tests. At 2.1 GHz, 350 km/h, and 30 kHz subcarrier spacing, the maximum serving-referenced Doppler-induced equivalent loss is 0.0294 dB. In the 2000-trial comparison at 350 km/h, the framework reduces the inter-cell RSRP-difference RMSE from 2.3449 to 1.9053 dB and the mean trigger-location error from 57.72 to 41.36 m. Relative to traditional Event A3, successful handovers increase from 1965/2000 (98.25%) to 1987/2000 (99.35%), RLF events decrease from 4/2000 (0.20%) to 1/2000 (0.05%), and ping-pong handovers decrease from 34/1965 (1.73%) to 19/1987 (0.96%). The results indicate that the principal benefit is improved spatial consistency of the handover trigger, while the other evaluated indicators are maintained or modestly improved. The dominant contribution arises from geometry-respecting residual smoothing and bounded position correction rather than Doppler power recovery. Field and broader multi-cell validation remain necessary.